{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VAE outlier detection on CIFAR10\n",
    "\n",
    "## Method\n",
    "\n",
    "The Variational Auto-Encoder ([VAE](https://arxiv.org/abs/1312.6114)) outlier detector is first trained on a batch of unlabeled, but normal (*inlier*) data. Unsupervised training is desireable since labeled data is often scarce. The VAE detector tries to reconstruct the input it receives. If the input data cannot be reconstructed well, the reconstruction error is high and the data can be flagged as an outlier. The reconstruction error is either measured as the mean squared error (MSE) between the input and the reconstructed instance or as the probability that both the input and the reconstructed instance are generated by the same process.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) consists of 60,000 32 by 32 RGB images equally distributed over 10 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "tf.keras.backend.clear_session()\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Dense, Layer, Reshape, InputLayer\n",
    "from tqdm import tqdm\n",
    "\n",
    "from alibi_detect.models.tensorflow import elbo\n",
    "from alibi_detect.od import OutlierVAE\n",
    "from alibi_detect.utils.fetching import fetch_detector\n",
    "from alibi_detect.utils.perturbation import apply_mask\n",
    "from alibi_detect.saving import save_detector, load_detector\n",
    "from alibi_detect.utils.visualize import plot_instance_score, plot_feature_outlier_image\n",
    "\n",
    "logger = tf.get_logger()\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load CIFAR10 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "train, test = tf.keras.datasets.cifar10.load_data()\n",
    "X_train, y_train = train\n",
    "X_test, y_test = test\n",
    "\n",
    "X_train = X_train.astype('float32') / 255\n",
    "X_test = X_test.astype('float32') / 255\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load or define outlier detector\n",
    "\n",
    "The pretrained outlier and adversarial detectors used in the example notebooks can be found [here](https://console.cloud.google.com/storage/browser/seldon-models/alibi-detect). You can use the built-in ```fetch_detector``` function which saves the pre-trained models in a local directory ```filepath``` and loads the detector. Alternatively, you can train a detector from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_outlier_detector = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'my_path'  # change to directory where model is downloaded\n",
    "detector_type = 'outlier'\n",
    "dataset = 'cifar10'\n",
    "detector_name = 'OutlierVAE'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_outlier_detector:  # load pretrained outlier detector\n",
    "    od = fetch_detector(filepath, detector_type, dataset, detector_name)\n",
    "else:  # define model, initialize, train and save outlier detector\n",
    "    latent_dim = 1024\n",
    "    \n",
    "    encoder_net = tf.keras.Sequential(\n",
    "      [\n",
    "          InputLayer(input_shape=(32, 32, 3)),\n",
    "          Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu)\n",
    "      ])\n",
    "\n",
    "    decoder_net = tf.keras.Sequential(\n",
    "      [\n",
    "          InputLayer(input_shape=(latent_dim,)),\n",
    "          Dense(4*4*128),\n",
    "          Reshape(target_shape=(4, 4, 128)),\n",
    "          Conv2DTranspose(256, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2DTranspose(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2DTranspose(3, 4, strides=2, padding='same', activation='sigmoid')\n",
    "      ])\n",
    "    \n",
    "    # initialize outlier detector\n",
    "    od = OutlierVAE(threshold=.015,  # threshold for outlier score\n",
    "                    score_type='mse',  # use MSE of reconstruction error for outlier detection\n",
    "                    encoder_net=encoder_net,  # can also pass VAE model instead\n",
    "                    decoder_net=decoder_net,  # of separate encoder and decoder\n",
    "                    latent_dim=latent_dim,\n",
    "                    samples=2)\n",
    "    # train\n",
    "    od.fit(X_train, \n",
    "           loss_fn=elbo,\n",
    "           cov_elbo=dict(sim=.05),\n",
    "           epochs=50,\n",
    "           verbose=False)\n",
    "    \n",
    "    # save the trained outlier detector\n",
    "    save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check quality VAE model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 8\n",
    "X = X_train[idx].reshape(1, 32, 32, 3)\n",
    "X_recon = od.vae(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X.reshape(32, 32, 3))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_recon.numpy().reshape(32, 32, 3))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check outliers on original CIFAR images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "X = X_train[:500]\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['instance_score', 'feature_score', 'is_outlier']\n"
     ]
    }
   ],
   "source": [
    "od_preds = od.predict(X,\n",
    "                      outlier_type='instance',    # use 'feature' or 'instance' level\n",
    "                      return_feature_score=True,  # scores used to determine outliers\n",
    "                      return_instance_score=True)\n",
    "print(list(od_preds['data'].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot instance level outlier scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = np.zeros(X.shape[0],).astype(int)  # all normal CIFAR10 training instances\n",
    "labels = ['normal', 'outlier']\n",
    "plot_instance_score(od_preds, target, labels, od.threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_recon = od.vae(X).numpy()\n",
    "plot_feature_outlier_image(od_preds, \n",
    "                           X, \n",
    "                           X_recon=X_recon,\n",
    "                           instance_ids=[8, 60, 100, 330],  # pass a list with indices of instances to display\n",
    "                           max_instances=5,  # max nb of instances to display\n",
    "                           outliers_only=False)  # only show outlier predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict outliers on perturbed CIFAR images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perturb CIFAR images by adding random noise to patches (masks) of the image. For each mask size in `n_mask_sizes`, sample `n_masks` and apply those to each of the `n_imgs` images. Then we predict outliers on the masked instances: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nb of predictions per image: n_masks * n_mask_sizes \n",
    "n_mask_sizes = 10\n",
    "n_masks = 20\n",
    "n_imgs = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define masks and get images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2, 2), (4, 4), (6, 6), (8, 8), (10, 10), (12, 12), (14, 14), (16, 16), (18, 18), (20, 20)]\n",
      "(50, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "mask_sizes = [(2*n,2*n) for n in range(1,n_mask_sizes+1)]\n",
    "print(mask_sizes)\n",
    "img_ids = np.arange(n_imgs)\n",
    "X_orig = X[img_ids].reshape(img_ids.shape[0], 32, 32, 3)\n",
    "print(X_orig.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate instance level outlier scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:39<00:00,  1.26it/s]\n"
     ]
    }
   ],
   "source": [
    "all_img_scores = []\n",
    "for i in tqdm(range(X_orig.shape[0])):\n",
    "    img_scores = np.zeros((len(mask_sizes),))\n",
    "    for j, mask_size in enumerate(mask_sizes):\n",
    "        # create masked instances\n",
    "        X_mask, mask = apply_mask(X_orig[i].reshape(1, 32, 32, 3),\n",
    "                                  mask_size=mask_size,\n",
    "                                  n_masks=n_masks,\n",
    "                                  channels=[0,1,2],\n",
    "                                  mask_type='normal',\n",
    "                                  noise_distr=(0,1),\n",
    "                                  clip_rng=(0,1))\n",
    "        # predict outliers\n",
    "        od_preds_mask = od.predict(X_mask)\n",
    "        score = od_preds_mask['data']['instance_score']\n",
    "        # store average score over `n_masks` for a given mask size\n",
    "        img_scores[j] = np.mean(score)\n",
    "    all_img_scores.append(img_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize outlier scores vs. mask sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_plt = [mask[0] for mask in mask_sizes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ais in all_img_scores:\n",
    "    plt.plot(x_plt, ais)\n",
    "    plt.xticks(x_plt)\n",
    "plt.title('Outlier Score All Images for Increasing Mask Size')\n",
    "plt.xlabel('Mask size')\n",
    "plt.ylabel('Outlier Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ais_np = np.zeros((len(all_img_scores), all_img_scores[0].shape[0]))\n",
    "for i, ais in enumerate(all_img_scores):\n",
    "    ais_np[i, :] = ais\n",
    "ais_mean = np.mean(ais_np, axis=0)\n",
    "plt.title('Mean Outlier Score All Images for Increasing Mask Size')\n",
    "plt.xlabel('Mask size')\n",
    "plt.ylabel('Outlier score')\n",
    "plt.plot(x_plt, ais_mean)\n",
    "plt.xticks(x_plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigate instance level outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 8  # index of instance to look at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_plt, all_img_scores[i])\n",
    "plt.xticks(x_plt)\n",
    "plt.title('Outlier Scores Image {} for Increasing Mask Size'.format(i))\n",
    "plt.xlabel('Mask size')\n",
    "plt.ylabel('Outlier score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstruction of masked images and outlier scores per channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_X_mask = []\n",
    "X_i = X_orig[i].reshape(1, 32, 32, 3)\n",
    "all_X_mask.append(X_i)\n",
    "# apply masks\n",
    "for j, mask_size in enumerate(mask_sizes):\n",
    "    # create masked instances\n",
    "    X_mask, mask = apply_mask(X_i,\n",
    "                              mask_size=mask_size,\n",
    "                              n_masks=1,  # just 1 for visualization purposes\n",
    "                              channels=[0,1,2],\n",
    "                              mask_type='normal',\n",
    "                              noise_distr=(0,1),\n",
    "                              clip_rng=(0,1))\n",
    "    all_X_mask.append(X_mask)\n",
    "all_X_mask = np.concatenate(all_X_mask, axis=0)\n",
    "all_X_recon = od.vae(all_X_mask).numpy()\n",
    "od_preds = od.predict(all_X_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1rpsYTcz1s1i2XaLP/o5XXM3YJH3hpdHrOnsCuzx8iFf4rOMwKZfTo+FhLWpq+3Y16XmzhYWirLJtq5RNTRdldn8pTjvjaKAe7tO54pfP9HGi4SE9Pe/MzXXc+KieNz0LAEjPnOm8d6TvDWRp537DR7P3MzZJX3hp/PqiMbvQd7mafrPmceptH9VodOy350SDA7p/+xYAQProk7o/xEw2O9txnTbM96WrVDq2s0bJCu/mnGjA1KNa7bgOatVi0y8sdtapB/m17O+GsUn6RVu/WRJnSKWf8KnpL8psAMpiC0C8fp1c5tTp7uevNiX1cxUTm0no6y+wbmstNqlsIIQQQgghhBBCSF/pqmzwsY62FDOhZnhisSEjnEupmZU0syAuzEBWIjNKmxnJQriYVSbMLy0BAGKnI1ou0npEQUURWWWBmSByZYqDHuQ1tiMvsZk9jYKKotXSEbVWyaRUqahimVE2Qi6EpUxba9aSFuxjjS2HTjVOw8zkVyIpT5zur1kFUWjMZjfSEFu+oW3am4Hn4hVhznGp/iMJM7mtlimLpCw2s6NJU7crVRnlrZqZ0EZFn6PRkJiMjWojD7m0amI40e00/O5cmRSJkAvEziYWqgAze+9qYYawZfrCEpVDNj9vLtp9JjQ9eUoOM0qjtpmR/Py22RKjdFpcLNnfSdtMaIhJVzXPOz1jDu6iWLD77LPRsJqsJla9ms+UGgWQq4YOrYfqyKodvGm/0enpjvPzmHF1VfD6MpWCuU6bsiJsF3Uzdbefu23nhO/oZcn707LYWyYe7fUJWVVK4qwU01ZzdUChDDDXAVTNU3r+hfY7PfrnXuW+1akoLL3+Zdw/UtlACCGEEEIIIYSQvtJV2dAyM6E+zFDa8ZsojI7a47JMR5WifObQGjWYGchaTUZ6k1hHfBfCbM9g1eSTmplMX5Jr7ku9FOzoLDoxI1FZuJb1V4jMTGt+fW8uVDbAVLaMKJcWJavBkslby/L4M202b6BLzh6nu5OqnFM1cpzE67H5xExUsdfM1Q7lcZDf3oibYERNxTvEXjMO0onExFZkZ5XSoH4y+ytGGZGF8VJvfGOqwZMhMsqGJDbqq/AcWbrM7BUhF4DNM837FOuf0HO2pgwbcyWzILnXwrL9zWr0QyFOy3welqNQfRgFRpvCIw/T5WaWCekXXWYY27wOLCUzjDbek2MnOvfnXgcm7tvUT7lioEyJZMp7zX76c/ErK1MdhXtGQ+rLUqoEaVHhQFaB81G4lfWLy6jJe3oYXQir/Xder+tfBsoHKhsIIYQQQgghhBDSVzjYQAghhBBCCCGEkL7S3SCyTS69vDzDuWVSGoJU0pbZVIVWQww7alB5WK0iy/eoJWQ7rWBq1ZY4Uaaa6XlAJ5lJzWjZOuf7vR2b6ZR5upL7rF1RC7mcqRofuKQi/2jLeMjNWU3KgT0gN4isVE1KQmrTpuRY4/2Kep6qULHt3C7pJ9vVqjFrtMtghqU5U3NPF8wa48SY3Zk4iuMs1NdK7Iw5ZiTSzsgYZuYrgKZpZ90AIAvpIul5mMkS0ovCbBHGsPE85I1thnDLmF51LSu7ppWHWzPJHJOeVZjQLXftkDeVL5sJoC29MctN6sr637blyzqXmSbkolFmMpcuY2AasKkGNo2obAnZtmt1KWs/oE9xcA5LzObvqrY0r7J0J6Y4kdWgX21+mevkMWvTgHoaM14uXAb9JpUNhBBCCCGEEEII6SvdDSLNvHxuClc2pxlZtYIZNYrDqKgz5m0pdHQ1940cMkZuw4PyMzFmHo1IR5Eb6JyNsXXyxahryazNOdCm6igpWzmcPSX9x5owopovc2lm/JGbnuphrm4VSEFlsIzpaSssSemM4qBSCwZSxvXRLnXbDLGXet0fGdVTMhDKEt3fCgqLyCyRmUZ6zlKY0B0w7wirIGrkyo3EPkcw5TPqI7tUbpaE5TIjztCQVaDEVK0nvQzhSmZXK1s26z/qooKwS0+mU9OdVTP1aes3c5O6aJmlM8sIM6Vtxls9lgAr7rOcGdhlYHRFnh2ULnOZK+hM2Fa2bS2202PH5RSrADLfvNGgdHJty0TmS8RWrEpg5TOq0YBcM2tadVOos42TsjjqtcSsiffSpS1tGWOTrDXOwVQyj9M2lWDAKpXOyUjyXJa8XMm553L+uVxzDUBlAyGEEEIIIYQQQvoKBxsIIYQQQgghhBDSV7obRBpFRq4ui434Mt8fuaj0HI/chM7Iqs35cSz7W6lKtZbmZgEAc4ePFGUbrr252G6F8ZHEKN8yI+XO72+N6XJVyjK2dqa+ZruXOWZPpYtvvzkhfaTZUClYKw+/qsZhFMwRU68yykpLW31SCWkSLT0nTvWaLshEI+NEmQXTq3jAxJYZroxC+kSUGQMep+0/bsk9bfLCQDg0US88VG10hvSItGmkqub+UTCkSyrmPvlm3RhimhdGK/jZJa21Kzkjly/WhDEaHQXQLs3M5ZzO9IttpmzFycuYuwWpZGbSJFxNAiid0TSK/N4AkM3OhgNNX236zbzObWkWQWa+KiZay/WLRWfN2CT9x8Zm0a5tnCWdcZgcOtxxvo1Xe81SCXaI3Wd++o6iaMcv32Uq1T09odRotSQNotRotS2P0jpIp211s7i6Gr5ao0tCVpWytlwWE71MT0sMXwGTPmHN/0Mfd/jf31aUbfktE5u9uJC/71bjb8M1/PcmlQ2EEEIIIYQQQgjpKxxsIIQQQgghhBBCSF/pmkZxaN8zxXYcXOWrxlHX1USi4mIds6ibtcGjIHGpNoxLvpGcDcRB7pKoFCbxcn59y66i7MyCStbmgxSsEps1yI1UOwvyG2fGUaJ8NQxr4d8mN5F6eJsiYvaWCVNcnldiJd9GU56nkGSu0/mUkAvl9OxcsZ2GlIlpk0aBwnHXxIGJkzwKYyOtrJrVGYIqG8OxytAQVcO52uYrJvazkCbRNKkbjaZeMw33j81+H1aN8Sb/ymRhFClMTW/SKJZMncNKHOYVgixYiadNfVclkXlHhNSRJF7hSgGEnAPW1T2dmgKga9ifvZ0TT052nG/71Wxu3hws52c29SKXWhuyeZV0527byzlt+5KFWQqZuZWlmrrnaRht6SAtG7xpx/ml17ErdoT3kfavhPSP2Kzg4mdCapFpv+mp050nRZ1t1aZOtKVmFN+Ymdkv/ebO3/myXtO63y8udtyyLZUhT7uyMRHipy39qi1lIs8nNt8EZZLzEkpTughZZaKafmvmaYE2hb1IBey1wkrbCi2dcWjTAvPtrf/tfi0rS9NYNh2pS7pHL85hJY3SVKuVpp2sEahsIIQQQgghhBBCSF/pqmx44Bk1acxnKQqVAIBqrjKwM53GkKMaZjKNxxyWzGDOpvExAMCudWNF2ZYBqdLI0HBRtmhmbVwmFzszo+ZYi03dn4ZZodgoLGo1GSX2RqMQm9HoxpKM5DrzHJEZdWoEk5zUzFhVqvKcgwOD5hwzwh1+JhzOIavAoaM6A9MIJopZrKO4UVA2mNCACYmi/Q7XNDgHqjpiPDIY2vXYeFE2GCZbqsaMccDESTOon+YWdeR4bkYrsBQqs2jfEQgXNQqKyLyVisHqzIxQW/PXWj67qvt9UDQ4Y3iZGbPIWrh+tEjVEek/8XV79B9TYtjoF3T2MjddszOIaQ+DyHj3lXr+4WPy085ihDhsU02YGZiea4eHe8Ub1+vp4R0B0+/Z2ZgszAzns1ByHbM/qDGs2iGfvYo26H38oM7i4oz0694qOQjpE+nWdcV2pB1aUZZ/F6YnThVlVrmTK4TckH73+fERPf/YSSkzqqI8Tq3SqLJpQ2flTB+XlSiVrPooyr9fS1UV0BlZXz4LHAXlhNuxTU/Zf1BOSUy/aM30cnNM+z4gpE9EE/qtibzvMaoftxTUDlYJl3UaGi+ntCtT6uV9pO2L4/X6jsh69EOFgWqJei8y6qSsqTEVj8jftlaZ6Kx6cDi8Y6rW3FKeIz09ZW5eIkdcw/BPYUIIIYQQQgghhPQVDjYQQgghhBBCCCGkr3RNo3DDE/qPIKey9hON8A+7Em9qjwimbkNG3tUy0ubhBZGK+RGVm0yskyptHVVZSTyhMrWT0yJr2XtcJWlPnlKpi8pIdb8L6Rx1I9WuGolqs7EUjtOqW1FMnkbRamnd83SSgbY0CmMkFCQutTYvsJtASD+YnlGDyNw/NTZpFEki21mqZXFFxxYrFYnNxKQXNAdVDl2JJWYW51TG1ggGlANGujbn9PpTi1I+Zc6ZnlGpWKMp5Ykx6Eli2Y5MjkebMV6Io0ZDz2m29J6VcH8fqVwuSeVaFSPprtQ1ECvBmLZWo0Ek6T/Z3v3FdlfJ8XIGUXlKhGm/zkqkcwm0kYhmt98AAJi6Ro3nmrYPDZ31+j++W8tuvFavmcu/jWw0XQxS7l7GciZFIxo08vKS1JBsSa6VHT6qhVYOuoYNrsjlj7//Ef1HmZw5pATZdKK2mJiWtCicOVOUVaCpCBgbleOs0WSI53h0VK9pviVzs0f7PrBS7vR0uJc1yytJs2hLkygzhLXnh2eOjp7Q3bk8fRkTuty0MjJmtoT0i2xKU+Pz1CObFhgNDsiGMZL05rswm5NvYmsAaY1WowE5vy12fGffVha7NiXCmT4uT39su89o+HvVpDHGtg9dJ39XR2f0eWHSLIq0RGNZgHF5d8RpeeqEGwhWAUtr19yVygZCCCGEEEIIIYT0FQ42EEIIIYQQQgghpK90TaOwMshiTW3rRl2kTJSs7xuOAIDESK0HjGwlykQGenRaZddZKNs/pfKvRqZSmql5kZtML6iEdCFV2ddMkJZGZhwlr3slshJNkxIRjnVGztmm5vS1UDfjcprfM0nNOUYqk8uAuFw4WQVaXttiVpU4Te3a3wPSZm3qj7PtN5a2mpr2m3iVlzVTabjz0NibC3LSRSPfNptoNaXNT5+eKcpm5vSaSUMObprgys/JIpV/RyOaUrHkQj2NNK61YGIuDqkZi2aFivBskZHgVYcGiu04ku1KiYSOkAtlxW7tbet1l7RFsz89pCtDFQ7b1on77ocAABP3LJOakcuhrSx61vSxz9kFAKg/oxLSKKygka86AQDOSFgLSbmVZ5etehHFnWXLpU7kHabjPAhZBUzMpDPSfp1d0SGkzGazs1gx9iMv9KfxNbv1mmH1GBsbduWIKLjPZ/PG+d5sF/UzMZHLpi22zmVxGNk0juDyb1fVyK9eOOx3bMu3RLZoZOaE9Im29IYQU74srW659MMS2tpviB9XsoKLTdewq7HkKQ1tdSurp01pDOkg9jug7Z4laVGWKF+tyn5HHA+rZiyzEkeeIrKWV4phj04IIYQQQgghhJC+0lXZkLaNkoQRILuOdr4usF2L15hauKB4SJzuH410NGYgHHpyTkdhl1phHe4pvc5CU88fCOZumRndGjbXbLbCTGiqI7/VMKbiocdlsXmOMMvijQKjbQlTn4+ywZxTNuLWJofovA4hfcKbhpVVJGacmfloBUVCZOIkNTOMaRi9rVb1OlFiZlJb0n7nFlR1lDRlRLeZ6nFVs5142V5qqSIqa5qZpPA6aZpR4MWgdsis4smrCqFVlTq7zLyLquaZwnMmZvI0nzSKzbrl6YKORmeZ1K+SdR8VJ+S8sDP53cwVl9uXmzHbWYxW57H5bAZg1vE214zHxortfBbXkhi1RD3EijXpaptpzSkRLpwXvYwge5lSEnI+mNiMgkrHzp7mRnCZ6RvsTGehWLUzlUb5U8ykXm+UDUEh3DbraOqRKxLsNduuH85zVS2L1peYNBoVYzETW3Ifi5/ufC/APLud8S3ubdRNhKwKeUya79fCOHGZ7zZrDFl+QPgedKb9ZkFFsIzSLo9dZwzMy9QF8bguYuDWSWy2qRFLzJKXo+jL2/54lPu0KTC8+Ru2zDB2jUFlAyGEEEIIIYQQQvoKBxsIIYQQQgghhBDSV7qmUUR2ne/CuKmzzMrQrIFk7hvpzJhG6nW7HolMZK6iJjUzLSkbHtTrVGp6/XqQkk0vqmRsuKrSkpGa7N9/RiU1C+H+VZM6YetUqM/bjKp0s8wHU08xkhtKP8lFwg9om08auSGiibNqSB1q6HGxNUUNxostzZJALdb2u/YgICIAACAASURBVDgncThQU/lXM5eMGSlXrTpcbA/UpHw0VZmayUxCK9xz6rRZtxwhNWNRy7x5LRVpW5GRkNp3UG6UOWzWNPYiO42NajVb0n+kiTy092vXTIdcxljjxiCH9mXSZNO/WoOpQj65TH9S2boFAJCeOFmUxWMi40xn5vRAI7vO5dTxnl1aD5taFOTU6a179D6PPiNlU1OmbueeemRTL9skqGXHht9Nr+MIOR/iEe2vitQi+806GIzWekmyDZk1Us/j+KFHe5xk2neIzWhITZLTspQHazB56CgAwN14tR5wtMd9ynb3kF/bOhVmkiWpFYRcML2MH0OfYFOMbOyVmgvb9p/vN+kJrt5ptFqW8tCWSlVCatIPK8Pyjok3bijKkkOHzcU6U0Tab5ane3Q+x7L9YrhWWdrTWoHKBkIIIYQQQgghhPSVrsoGO5XvS5wOy5Yl8e0uigCA1IzgLKV6nWROZma8Gy/KqnWZodk8poYcg2bGducGGS26apOOuA4PmFnNcPnPPKnDvJ98Qu5zuqn1iNGpxkgSo1JoW43LdRRac46cMt8SLn1JVoPEmsNkYUkq6ChtNhfK7EinURVFYWlLZ8xVK04VRvVBCaTBupatG5WY3DaxvigbHtF71usyKxRFZuTYTBA1mjLrc+CEzpQ+dVBi8+SUzsgmJUHTNO8A4/Na/B4iY4QVhziNzaC1r5jl+WbzpW57vP4IuVDy5baqJSrBZWYhipkVEweRmYHJFQ3RpJrENW65EgBQmTVLxA4aNdBnviQbFb3n7HO3F9unr98JANjxew9pRcIscDwxURRlc8Y0MjdWtrMtvWZjei1VtoaX7iKXP6Xty7RJO0O50uvEJg5dXfrIZNu6omxul8TR8AGVEZ6+Wb9fN31SlsZMn3hKr7nnqmI7m5RvYn//I+b+Yab1kb1aVjbruVKz2mXOyRa1zi6fWa7VOk4hZDVwFVXJlqkL2pesDDFpxfWm33TXSEy5Q8eKsrw/izepCsEbs0e//6AcV7akM6CxYmIrVzFYVVBZv7ecCiFXQVoj1uBj2aYSbDsnPPtaVgRS2UAIIYQQQgghhJC+wsEGQgghhBBCCCGE9JWuOuJWagzl8p9GJlmWRlFm8uGtAtrcsQqRTt8+oVKXW597OwBg05gxATEXqAXZyo6NKjGJrIQlkf2V6zYXZTOLsv8je1W+7b0x8QqpHRVnJDmRec4SYxGkIltJzb3tyI3P0zRK0i0IuVB8S9tdmrflVCWPeZqPNw6NzhrjRCKFjBKT1mTyE+JgyDg6qJLJzUFedsU6NdkaHFGp2HgwYHXQOKo4fUcsBfPXkbpZQzzcMzPpVdOJeUk0pdwZSVli0jQGl0KdMzW6qqZyz3pNr9M025mTZ/ILJWuME7IKtEkms5KUxFanvLtNQmr72nAtN6wpTlN7pE1PPm7MlJ80ctEB6WNP3KEpUJWGHlsNWUzT3/Gcomz0b+4BAMx+7wuKsslP7zeVDmmSp85oUVpmyNX5nWAlpr6pqR9MoyCryUplxlZ+XWrealNqrZnjpKQmVQ6fLorGz4TUozOaorH+7lPFdl6j6Jbr9f5z2p9FU+H83VfqOXv3h53m+zLpfK+0mbP2+hYNzxQNGOn5qErKs1PyTNncHAjpOzYFL3zTlhm1LpuWl/c35m8ybzIvTt4h6U6Tjw8UZbX9JwCcZeB46BxMkLukJrWlXpSkBrcZXTZNRcM1s6XOa7ua1h32b9TwO4lKDC/XClQ2EEIIIYQQQgghpK9wsIEQQgghhBBCCCF9pWsahTfS5jyVwEfdV6DwRiLqgi2oh5bFFZWBxKO75LghHfNozIvU7HRFpdqjQ3rOEydE+vyFxzQlYv6USmCGtojjaO62DwCtBZGojBj59VJm5GVOfg1tohWjv0mzTulcFpxRM/O8ldg4jueXoeM9WQ1axqkeIqF2VeNeG+SeWdOs0hBn5pxWOMdIL1sqwVqcEUf8BSPDbAxJW26YtbmdiYnFXMpl3KzrVj5eD+8DkwIyEOKwamRkWaNptqNwjkrSosjElJd6xoneJzf+r2T6PI1UpbBpuGdrkZJtsgoYyWQh+bQpeLlctG3lphLJpFmBxcos4yCnXtyljvcuNOX63uN6zrpR3T54CACw6aPPFGWnXrSj2N78eelX3eNPawVG5fwR46LvjTt9ce+qiUfzvshd6zMjM4/CGuSRcf9O9mud9MC1u144uYyxEuzQxtrab9hfrAgDwFXt6gtyjpVyZ6Y/jA7JKmht8bp9i5xjyipbNM03OSYx29ik37wv+DNdeeJffv8bAQCbPqVpUfE6kYSnpzRdw8ZMvF5WkMmmTYqH6+6In0vWbQpJZlbnyFOc4vX63iGkX7S1xdBftpWVrYZilgAsS7mw/er699wdbmRWchsJaUI2hcNmPITzK7t3FUWPvkNTEW/42SNy71lNLcrTJ3qlBNq/r9v6/y7ph3YFq7Z3VJ5yaf4GWGtQ2UAIIYQQQgghhJC+0nXaPYY1l5FRFjuTn5e1KRvMtisZoXGZjrwcWJDtx6Z1xPeRUwcAAON2VibV86emZWaldVBHfitn9hfbr3mzKBtOGMOPq8fDbMqAXvOup9XUKg6XHzcmcqN1MzsbRtLsKFsjjFIvLuhMz7Qx9DjRoKKBrB6ZMXvyA9Iu0zYn1tCoUx3tTZtmpDUMDjfMOR5qmBinYdZnSRUFlXkZSfVGERAbU9X5EzLzMtPQ/RNDOksyHGZbhs3I9JlpGRGuZhpH42bWyE2Iqmm4auJxXOM4qozJs5lx07gq5nPzic44HZ7WZ0uOy2xNmp2DERAhK6XNtCr/WTKu38Osrsw0CgDSJ/cBAAbnNDbrnw+zKVds0eOGjbLnO54PABjar3EQmdsnIxKTsVEhTP27OwEAGz59qCjzWzeZk4KSydQjm9Hzy1QQualVdkRnaXuZfBHSL6wiIVcHwBhAZvlsoZmVtDOmhamb7zRjBAAXzBWz+fmiLFfuWEM419bmQ0zU9R3xi5seLLa/eN/NsjFlVApbNgIADv6Imkru/IOHi+30pBpQ5uSqIkBnXa05a2WnKJ2SzRNadkivU5joUXVEVgOrigsz9W2qoxCnbYatZf2EUS60GSbms/7mb1iruiut0oB8fyabxoqyp/7Ne4rtV/7ki2XDGJgjdIfu9pu1SkYxmBus+kT7d6smKtRV5vs0VzNaU0gcPFJs5nFs3zFrDSobCCGEEEIIIYQQ0lc42EAIIYQQQgghhJC+0j2NwhoiQrZrsZ6SBClZw0rO2kwtwraRajtjw9gIRm2nllTWUgsmi6NLKkMz3m4YWToJAFjyKgdtGUlbckakJUcPPG7qKRe481tfXpRtGFTTyU0jIoHZsV7l2YNVfY6BukhMK0aikptGJsakY99RNa3848/uBwAcKVkrlZALJR6w8rKwPraRWGVZbmCq7Tgx6Q1IJM5SZ9IsjDqymYkWrAYtTEOcz5nUChip9DP7JObOHDmq9TTpD+u2yhrkmzdu07LNYhS37pqtRdnk+GCxvWlUJJ3rNuh63xuMYddiTa6ftfQd0AiGtEdOq2nPfU8PFduPBFns6SYNIsnqkqfeRSMlEualhjnQuFLlfWiZaZTZv3CrGjxGiZTVv/BEUVY5qdLM+CHpTyNj7jb+j2rM+OEn7wIAvOyK5xZl6z/4GADg2PeoVHvDn97f8YxtMnMTm7kM1Mq3XZClnvoulZhGb1RTy7FX7JXj1rAclFy+xJs2Fts+pNa1GaEbaXNB1GlSZ1N/oiHtW9rM64rCELsmlcrKt93X3RQK9ZRXbv/6Yvsjh98HAHj5VXcUZcdedw0AYNMDGntWXh4Fc1dn5d02bTB8K1iTueRpSWF2RzUesVGNXHNjyPTEibOfkJALxsZRjm2fRWyWpSS2nWTS+s3fpvEGMXbMTEptmRmj7XuSO24AANS+qikLto888NPSN+76gMbM3rddCwC49jef0utYk+TwnDalKxrTNA2EZ86NJgEg23+g43nKSO2zrTGobCCEEEIIIYQQQkhf4WADIYQQQgghhBBC+kpXrWLNOIG6YF09PqjSzIUg3Vw0DtR29KJsmdBabN3vRcJSMWkQV47J9W80jrinz2h6wvSsSEtaRmJ6fEbl0p/81KcAADfffmdRVq/Lc0yOqExnx2aV020MaRTWOT9yWqehAZHGRabuzeAUPmWcuB8/oCtgpC1xwncZnXtJ/6k5bf9JkIGmTtt33uyaTePMa9bgjUJqUdU4ANdjbfPVSKTPIxMqC900KakMw8Paps8sGjdrL9dPE02B8pl5I0TyntgypvW44QZJn7hyg8q765Hec3REYnfApGPE5jlyZWiS6MtmvinPUV1USewTg7q/tiTPXndMoyCriwt9RnLjzqKselAc3pNnDnY/1zhUW9f4nNkrNQ42ffJY53FmOwr9XTah6UjZAb1/IQ01/WrzVlnZafM/7dNzBrROuau2xaZU5FLuaEzTE/243H/gjHl//YlKtV1V5KKRSXMkpF94s3pYthS+0UzqT+lHa6+VUay7/Zz0ffGeq/SSwTXeri6Tu9wDQDIc+s0B01eatKlX3vIS2Uini7LZq6Se2z6oKYtpw6ZlhRUurNu+XUUifHPHVr6ds32z1u1RTcuy6VCE9Ju2tlqWKhFi01VMWpNdFaYkdm1KRHpM0n9sH1XEhM1SNOkN1S9KWl96taYs4qiupHTlr94r+805O/5lXMpKVoQB2tMjirqdPNn5HGWplcustFGkV/RKMbmErN2aEUIIIYQQQggh5LKkq7JheFhnSuNYRlZOT58pyhaaUpamncYzAOAK8w0dfYrseuGZjMZ8/RWqYvjma2SGMzNmdtOmlmkio1ILszrKOzI2Xmzf+tzbAQC3v+CFuj8oFpoNM+tiBo3gcwMfLaqZUaNWS0akD+7XmaBP3/cQAOC+Izoa9+iUPtt0U0aBo4q9ESH9IU6M6qgi7a5S0ThsLsr+ujE6jexIKSS+nInX+oDOQG7YJLG/fVzjYMNAHO6t5zRGdZR2bKPMklTclUXZpgm95lV7dgEArrtmT1G2c7vMuE4Mqilk4s1a6C7U35h4tbzG8cK8lB+e1Rmr2XmZ4dl/WkeWjx5WA5/Fk2Ki03SdI8yEXCh2NsXtEDPU6PNfKcqSHiZPxbkmXq3xcnydxM/mD+na3cXMhlX9mJmiLJi/Rcd0RnXudWo4N/IBmaGxs5/Vex4BAMy/5DlF2cDxpWI7elhmfexMjTX5inK1kjGuSyYkzg++WN8hY3t1e2L7Fjku1JeQfpLNm7aaz9SXzIiWGkWaY22M5woJAGiEWBk8YGZpg0msM8pBe0702QcBAGM7dfb06berMveK37hPjjPmrle/7R4AQHq7MVod1djzTx+Sn23mrVrn3CwvOaRq3L3vuw0A8KO3fLoo+6O/VVP1K3/xbrkOzVvJKlBmfpj3dQCAqWB+aIxOve3jTEyVXTPevAkAkB5XFUGhYlhGvZQGpVLlpP69Of+K24vt+oe+AABwJg7rd4uxctarnzf9e24kbesUj+q389H3igJ4ar/+rXz1+83fs+EdUrnqiu73vIRQ2UAIIYQQQgghhJC+wsEGQgghhBBCCCGE9JWueqiZGV2zMw3r2DdNroEPEuzaMlfxCOtsm7JCFg1gz2aRsb35RTcVZdPzIoU5M62mkJN1vcGhOZGz3HLzjUXZHS98sR67bhIAMFhROWndiyRuckwlpAOm0rVI5C6nTur6wQ8/9nix/Zm7RbL2uc98rig7UxE5y7pv+I6ibCExEtbcwC+jCR3pP60FlY8lIVWnatpaHjLWPydOjMwsbLtITaXqA2qUtX1EjNyuWqeGcusrwewOKjGNW5r+UN8mMrUNe7YXZVdsU7OpbWNyrcFRjb08namZ6jVbLa30XJDGnTissupnjqgx3Ve/tB8A8MihrxZluVFmVFfjuYVYY3MhSGGrcQ/jL0LOAyvdTPeGVIdeJnM2xSmYPHmTOtR2/aclnW/f27++KNv16yKjLDOfAoDsRV8HAIgPm/TD93++8/7mhZEFw7n6h+7T46zc8znXyc8Bkzby2DPFdnpApNzRNWqWN7Nb3hfX/64a23mT05idkjTNyKRVEdI3TBz6YNjojOlpnrrUlkZh0yyK/eXfdcMPSVrC6W/SlIjJw/Jdmc3Pl57zVEhf2PT3Wo88dQJQs9T0mKYC5qaW/j5Nz8pq2n9HO0VO3bhaUy8GP/1IsZ2nT+z/RU3XGLpPnu1jv/jcouyqQ3p9BFl3mVktIX0ljzNjup8bsdqUQm9NUUMKYDyiRqap+RsWwVgymtC0+/TU6bb7nc2Bn5VUwys+of3q0Of3ajV3SJz5Wf22ThfDt7VJxY9MnVxINUy2a2z6e75UbMc3XgsA2Pum9UXZyD/Jzxs+qGbN3hjTIvzdi3TtftNS2UAIIYQQQgghhJC+0lXZ0DSjJPkSI5WKNbUIo09mMCUx4xe1YhRYD9g8oqOv3/X83QCAK8zyegthGcvNxlhusq6zKRuGZST2hutuKMrGxnWEqNkMszFm1jIKyobTx48UZU/v19Gpe+97AADwhQceKsqe3PtUsT07J6NjKbQek3e8BgCwmKpawiU64lvNl8n0HM8h/WfpzOliOwkGp25YZ+8HcxMnYxpZNWZRPpU4c6m2z/Ft2r53Tsro75ZxVTZM1OUdUEdmyvR9UHGiJNi4TmN33aDGdhzMKuen1cxxZknMeo5lOpO0f5+ORj/6mMys3Hf3Z4uyI8bscW5Gfg9NM9tSGxGDysldqpiqbd1UbMPLs7kaVUdkdXFh1t77HstYGWPFbLHT6Mpy+vWiUmhs0D4uVzTYZfysOVztcZkhSsyyXZZ8GS27PF8c3gHZtJkdMktr+S8+LEX2QpOTes0wg+QPqophPCyll1hzLDMjW8zMxFwymvSfyi41L8aSfCump9T0vFD2mNlTG1O5ysAqG6yaqLkrLDFrvpN9MJkrXcoOwNW/LjHnv/igqahZsi+Y4MUbdbl2NyzKn2S/KonaZnnDMw08pSay0WbtA114jqt+/gtF2ZO/KsZ3B9+l977iLUa1FGZn0x5L9hJyPkTGEDFXz2SL+q1Y2m+a5Vj9tbtk95RZktkoG3LT4TYD59DP+KR8Cc1dfyvfmulXjZphi6p1s6CMiDI1box3yzsmfUpjs01xkP9teO/DRVHFmMP6Y/JNvOsdes8DPy0Ki+n/od8JI/9Fnz16Qu7lh9buktH8S5gQQgghhBBCCCF9hYMNhBBCCCGEEEII6Std0ygc7PrDIhtz3horyvb4kMrMGkZUma8nHrdUQnLFiI5vXLdVJJeLS2Yt4FSkYMMDKhHZedXOYjvaLeZz9Zqab6RNldrMnhTJ5v1PPlmUPfywyFW++JCmSex9yqRJzIY0CSuNM7KXOPwaBtarfGZ0o9SjTU5nDPp8kXJRbvJFyIUwMKJyqeiMxMyQUWqNhJSHwWE1WnMVjdNKGGccN2kON+9SmeXN2yV9YnRQ42wkhH5qZGYbJvR9UAnvg+GqkZCaOh89LlKvhx59rCg78oRIMg+eUHPWEyc03emJIEU7c1rTRrKGxlQayR3iIU2lqm3dJfXYfXVRFo+qKRCcpJukS0YeTki/MDLPvH+w0s3cLCqbU8O4XNYMAPEVsqb2zHPUIGr8fk1FGDou17zu7SrDTL/hVjn3UWPQeEb71dL0iUhTFYo1ym3dQ0rF4svViPLwN+k51/xJML7br+atVlJeGMlZg71AvF7j1Y1qqlYubfet1tmnEHLB5FJqAIj3iHGpm1LT1KzRvd0VKQ3jY0WZu/FmveaSxObEe+8pyhZfIekJQ/drSoM1e8zTkcreG7Y8NX0kTkrZqR9Wg8f6d2uMD/2S1K9yn/a1yWHtV/PUJXufwSPyTbD1rXpOYTwHkw5CyCqQzarpeZlhY2646Fvm7yzTfqNnpH0np/RbsWL+dsxTjrz52y56zh659l59L9j+Kn38ybZ7A0ByRPvivDyb0bpnwXzV33lrUXbqGk1/2PAZY3qZ3+eQxqbPwv2Nme3QcSkbe7MunJCeVrPILE8HeVz/rl1rUNlACCGEEEIIIYSQvsLBBkIIIYQQQgghhPSVrmkUdbM2fZ4VcO02lVpfvVXccXeuUwnolJFaTYftWqLu2qMtdf5tLolMpNFQKczoqMhNhuoqO3EmE2F4WO515ozK0P71Xz9TbN91l6wd/uhj6uR5MjjzNhN1603tGqVpLptR+Uwc668mrkldquvVydiFsigzKSDmnHz1Du/peE/6z3XX7Cq2F5rSxq4wK71snJD0iRGzWkRm2nxjXuSiwwNadu02ddRdNy6xP1oz8u+QEpSlZp3jzLrGSz3mZjWtad9hlYz968f+GQDwuXs/X5TNHDsFAFhsGema1zotzIksOzXhWolUdp2nRwxtu7Yom9xzCwBgZOOWoqy5qPWcCzK8iBlOZBWYecMdxfb4E+KMHR3Q/iqXUMdm5Yb05Cm9wGmRSo6aNcanvv05xfboP3xRNgZU2lk5I3HS5khvyB31faIy8Zk3PK/YHj4s/Vj1XpVQR8PSxw1/7omi7Jq79X2Sng59uU2TKFuv3KRrRDV5r2Q7NDbjM0aCeibIRKvm24OQPnHyh19QbI89I/3AwFGTnhDSiawzfpu8O+AG9Ju39hsni+3Gi0RiXdlxhR57Inz/LrPKTH4ve58nf1vrueVzEl/jn1bZtJ+V98rGv/iiXugvde4wWxA59XJdXJ5G4Uy8bnv3XQCA+Kbr9D77TIpUSHEqS4si5EKJb7im2PbVkBbwiKbD531b28pNJm0vDWl7kYnNJ35kW7F91U9LGlM8pilQOCL9bjpvUhrN6jPxBklltP3z4qufX2wPf+JRqce81qM4/25N25+4W2+Z5P2hSZNAZL6zQ7/rl7QvX/9H4QLmmyG2q3eEdBJvfh9rDSobCCGEEEIIIYQQ0le6KhtedIuONE0MyWjm1Rt1VGg4GG2MV3T2vlXRWYzFYZmdSMyoUWPBjG9EYdvpSOlQTcqqkZbNndQZnrnDYur28c/riO57P/DPxfbJ48G0ygzpZmFMJXNmhsXrDI8P47+uqjNFNaOsqIXZmMqm7XrRShg9M6NTGXQkqhgx9mb0ipA+8cI92hYXgpnaYE1ja3w4xJExw0mWjLFOLRgrRhoHyYIqEpqzMkKaDOo5uRnjqTNqrDi7oMqe1rzMzJw6pCY1935J4/Teuz8LADh5Qmd5s7xOVTMr4zQOMy8xG9XN2sg1E5uDYtpaNUaYjZbE4fGn1CioNa7n+Eyes5JR2kD6T23WtKuHvio/J9SgtJg13aQGkJVJ3e+DYiCd0fXCCzUDgCi09Wxe4zV7TGaAKmYNcLdd1QPpelEDVQ7oLGzyJo2P2k/IeyI165oXa5w702dnPfqz0llP/X0URpQPPqL1KDtnGYUGIRfCxvuNKfBDj8tPY/6WK3Pa1AxGmYOgurOGcEu/cEOxXV8v/WFy8JBeMjeUM6aoVg/ogtmkq+uM6j+95jeL7bf+xuvlmsdVgRFPiAoxnVLDuHNRHOTmrVYJFefvqOPGjHkNz5SSZxmH1OA0b5dtRqmBZdtkaP9uUL8FNzykMZGbNKc2tnPD140bi6L0pPaRaTCbtAaRjTGN3sFwfnTbjUVZa0L+NqwdVBV/+pQaN5f1ofY53dU7ZONhVXXk/bo1erZ1Wk7RuJagsoEQQgghhBBCCCF9hYMNhBBCCCGEEEII6Std0yhe/7yriu1aXeQoTx9RKdddnxJjxps2qWzFmms0Q3rE3se/UpTtuUaN3KJgKDd1SM0c58/ImsdHj6jU+om9uv9AMOpIhlQium671tPHIi1JmypLScKQSqNl1h1fUCnNYFWkc5FJeVha0NSPdGCDHDep5pg+Ffl5YiQxHrqdp1GkKQ0iSf+ZmNA4G09lu7WkBlRJMFY8PaWy0TPH1eQm8iLRHqgaSdiIMcWalzg8EamB1HQwrvvSV1XeNX3CmGMtyjkLDV23fH7WyDQHRKY5sU6lbVm4fL2upj5LVa1Hc0littkw0jO7BHlIcYpqam7ZDLfMMr23n9Z0kdwcdqCi7wNC+kVqTFmP/NjtAIDtf6HGi8ffeDMAYPPfPV6Uzd25p9geOigpP/FBlUxms5pSceTNNwEAtrzngaIsGhYp9tQ37SrKxv/l0WLb7RMZZ2Lkmhu+d1jrnJvXlZg9VrZqaoY3KZHpVIhzKzMvS7Mok3cvJ/kuM5gkpE/EpzWOZr7zuQCAkY/o9+k/HrwXAPDa57+6KPOmX82NFU+8TL85J//83mL7mQ9cDwDY/t0qoS5Yr31U+qiarroQe9Gg9oE/cdNLi+1s/qAcV9HP9TIpeXTz9VrnxyWV0ZtvXnu+DybJNu2qzJQ1GtZ3hAvGfLm0nJDVIo8zmx5w4h/FuHTid9QgvPrxB3A2B96iaU07/kzj7NSbxBB54s/v7jjHxnhb3xT6tsWX3loUTX5Fv6mjYCCZDGrsxHlqsP3eHjamlrmZpDFCjzdpGgdCv+/GRjrPsXW+DFInLFQ2EEIIIYQQQgghpK9wsIEQQgghhBBCCCF9pWsaxaLX3afnRWby2BFNP/jcV8RR+uCQykHWj2hKxXhV5CRjZj3QwVF13T54RCTYTzytcpP7HxRZzBMHdQWK2SXj7l2RNIkXf526f77yht3F9kAYPhmoqVPnoeOSknHwuEq+Z+bUdfurD4uM7vH77yrKMuPiX9sqq3JksV4zXQhSMrvChUkh0TQKrkZB+s9QTeXGi/OSInB6TuVd+x4RF/yj+/YXZW5RU6DGh0XSuefGq4uywZrGbn1cJJ2NBZVqTS/K9sxRTXGamddr1kZFcnnbbf+mKNu9U9cbH94obtxJqpKzE/Miaz11Ut8r+47q2t5PfkVkcM88ppLzBa9S2CiW+GvOaJqE2xRiLrapJrsoRgAAIABJREFUF/r7crmjuOdYK+k/Y5/SNKOxIMlMT6useiZ0VxvOaLrR8D7dzmWcdm1v6zy96Q8+L2VX7yzKnvwBkUNPfFUvM/UylZPObZe2fsWfaTpHNqcpEbmEOzNpEvkqFN6sUlOkTsAeprHlL3SBl1zCalMzCOkT2THtr0a/EtzpTUrCH0zJt15yRFOY4t1XmvPlG3LD541jvUkd2v7ahwEAZ77vzqLsDT/1EQDAh3/sOUVZJbqu2J66Rfrisb/+glbUBFJl+za5zzHtd+NtkkbshzT1Il+RxlLZvUsvaVbQcGPyTX7sxVuLsvXvuUf2xeab1ny7uzjq2E9I3zB9nGt1pri2Uml3Awe0D8pMW8z7yPH9JrXdrDwx+Vf3AwCiPZoC9ehPSvrCDb9rVnV5VL8v87Y+8M8am7YvzkJaortHU4vidZOysUVTI7KvqhVAfn76Av0b1s3p88aHpd9vXWVWlrrrobBh0gzPYfWZtQC/tgkhhBBCCCGEENJXuiob7jmsszGNJZnVPHJMR4qCXwxOL9hZSR193TYqBhevfc03FWU3PkeNNmqDMmq6fuuOomzT9TLi+63G4HHTOlVDTAxKlccH1XCjPqCju8NhuxrpOMpcMNI4vaCjR0em1BDk0xvFAHIx05Giw6d0VsnHUr5wWtUWaRhgGhxSEw9vZmNyZYO/zEafyOVBnGi7ai5Kuz62X9vnI/fJ7OfsQZ3qHB5Ug6odt4vK4OrrdhVl127bpscOyeiruQ127JAZnh1Xbi/KkpbOhI4OSzzv3K1qickJjY/BusSuCTPMLsko9OE5HU1+bJ/O2Da81PPEIX0fLMw/UmwvBcWVr+vsVDQv8R7FOjsU2TityjnVVN8bhPSLw2/UWcv1D0tbrD+gsy27/28xqKpcYeLoUZ2VzL7xFgBAZGYx2sygQnm2/2BRtPNDYhBZeUDj3RrCTQTlgjV3s2bOpWuXhxlbq4CIb9Jna02KEqrykM7aeGNklSse2tZKz/vIMiNJg1VLENIvnDFhTJ/cHwr1W/GjLxPz1cM/qX3QFb//YLF96nXy/brhXlU2lBmkTv6ZmtB97AtiEhs9qteZ+Z7nF9v1qbTtXKB99jRXNNg4SvY93fFssZmxdQ1R+mVj+p28dM2GYnv6KlEXbv2IfjMk4Vu17d7TqpbsFbOEXAjpiRMdZfGE/u237bvku8/fpoqAtph4ofSb43erMjax5q65ouCpZ4qy698mcZw19W/D6TfdUWyPPxH6vnu/XJRl1kyy7DnyPtaoGePJSd1/Rsqjz3ypKIs2rtf9V4giIn7QmMiG8zOj1PD2Q/qCJYWrD5UNhBBCCCGEEEII6SscbCCEEEIIIYQQQkhf6ZpGccbIQHK1ikvViK3mRIbZjFR2tWWdSjuu2HMbAGD3rc8rykYn1HAmCqkOYyMqmdy8/rpwba1HZCQiDkHqBT0gtakKqchNm4mRUAcTx6GaGtNtHtdHv+N2kbnVR1Rm/k+f+Hix/cxhkaylmRplJVWR40WxXrMClaVGQVrnuG44WQVaLW3fM6lIvVpLKndutkT+2IpUZja5XWWUO26SNbmv3LylKJuYHCu2qyFWvJGYDo2IJHODWf/XGZPF2nCIhYq+D6oVbf9ZGNtsZjaepWxkUK954y4zBurE5C5b1LIH7tLYnW2IxLRlYi+OZH/sVd6aLep7K1qS/V5fRYT0jdk7tJ/Y8JWQ8mBTEXJJZdV0v0ai7POYWS4FLzduNCZa0WeDRHt4WK9jzvenxQDLpk74EhOuMrwxOU4f0TSN6gZ5n5x87c1F2fr/+ZVi20o+teohtaKH6rNNIkpIn5h74Z5ie/TBIwAAb4xa0y2THefYFKPGRDD+fvwpc0D39IL0MUkzijepYdz4p/fpfmP8qIUlKRXGDK8trSo/5Um9ZhTeA0/8R01R3viAxtTmv5Q4TUyM5ufY540G1TQ6pzTlipALpC3VYCr0V8agNN4s8XPqFk2tWPdVTROKD8k5mU0VNKmEPk+VsP3iovTV1gh18u803alXykSeQtVmkpz/sWzuY/vCPJ5dTfvitnfAcUkniSb079Gl50lq8uCXNXUyPanP6VtpW33WIlQ2EEIIIYQQQgghpK90VTZsHddZklYYaW05HW2ph+XznjGDrLVxnT39pm9+LgBg3ajOWraM4iDzcs05M8tRq8j4x6gO+rRX2MsIUhTrOElszaTymVg7U5SVmDWazYmwDNB1V6vBziOP65JAhw6JsiEx14zDCJL3dikSsxlmbzk/Q1YD71WxMJxJmx8wZoxbtoYZnHXrirJrr1dzt207ZEnKel1Hhn3bEnZyzYZpwS4oBSo1NdkaHNQ4rBQqH71OM9XzW8EMsmGFCyEma2Ymc6iusyk3XyHL/zS/WV8I81CVwqMPi2nQ3KLO0vpFib20omUV6Ai1Hw5GQT0GrQk5H/b8nukn9h0FAHi7XNbVYgzp71MVwNKr1DBu4IP3AjhLhWBmOuOgLMoWTZsOszbtS1eWqOpsH2j2R2WzLflsjO33jGFXNiPqqQ1//7CWLaqqIwpmzZmZhfVcCppcQoY/8Wixnc/qV3bo8sx4UJaG3Xa/ttNTP6TLWG75r7I8erxe+1VrulrMdFoVQlAQtc1emhnIXFHgm62OcwCj8umhoIiGtC/P1QfXvFNN6Oy7wW2UWWJnjPHy/dYgMprU7/3k8JGu9yfkQsiNEwHtO/yY/g2aPizLn0+YZSSTF96m53zmi/LTqvusAqhEKZirEOy923A9VIYhJnsq9axJcti2dYtvvLbYPn6nmEVO7NX9tRPSr2ZndInOUmXiGjaKpLKBEEIIIYQQQgghfYWDDYQQQgghhBBCCOkrXdModm9Qw7g0E8nGVEVlGgvjIrG6xhh7XP3cW4vt7duvBAA0WyoPi2Mj1e7YALIgGfPG3K1iUybC+Ej7OtzG8COXu5SoXjJjTJcZ2Xa9IvcaG1J5+J4rryy29z4lZkAHT+uaw74SDCKdGkRaM8ioMMJiIgXpP2a5cJyaknY9adKVbnmuGCuOGknZ5s0qgR7fELar2mYbqTFdTeWaJvSQhDgZNO3cGkhqGobuT4zyczFIvExmBaI8XDON98TpSSNDIuncY+p+zZVqenXomMjKFp9Qw65mSM9Ca64oaw2qNLTu5bVXH6N5K1kF7lHpsrtqp/w06QnJAyLlttLJoU9oKoILEu02ebYhnZF2HQ1omy7tZZaTfub3qWjfZeXSxf4g87RyTyv1jsJLKK9Px+17mWsVN7KpiFJn+2yE9Atr1Nb6NknzxZy26TMvlr7lxJ3aB93wNo1NH1IVlovNQlZtinKTujYptZU756lFy0mgVyiNtmZ40UhItSoxaZUDwvdpS+tU2Spm0bN36LdvvKRPMjgu17QmsYT0C2sQiS2Sju9N3xBfJ6nBc9drCtPg/3ev7s/7TWP42qsP7Jkm0ev8lVx7uevY/anG+NIGKa99yqR2nArml+Y6eaoJYIwsL6S+qwyVDYQQQgghhBBCCOkrHGwghBBCCCGEEEJIX+maRrFhVF3hW005dG5BZVdDN4sMbYdJt7hut64lXAtjGZFZT9yotlENyumKWRrUBQl2xRm5iF1sImxHkY6TRG0u+iENA2Y1ilDllnHwt877MaQCw0Zqfctzbii2c0f+f/nsfUXZ8emlUDdzHWfHblyoL6XapP+MmRUhNo5LWx+pq1S7GmRo69apQ/Wwyb0YDG7ZlQGVUg/FGqdpJLKuyJQNh5irVMpXgonDfhsFSaxxnMd5YpReScipiCMT72aFl6gi21vH9dl236ArxRwMLsLzJ08WZYu5pGzJpE2ZN10UVtCIh/S9RciqsCQpCMmRo0XR3OtfAAAYe0ydpdtWkbDbOXb97CDVvtD17u3KEPnqSc68I/LUD28c6+3+nMpO4+Zv0izSbeKqbVfdKFaLstLwMulnvHbXCyeXL3bFhoEjkv6TPbm/KJvMJLVp3Ze0faZzJh57rAhRdly+YplNc7hgwndl26oXNk5HJH0yyjSNsm01ilCXeET35++ooUP6De9t2vPTh9rOJaSvtOXsSvy4Q8eKonz1pUGzGoVdsSmb1jT3FZP3Pb3+Tlvl9IT0iX3F9vZfl21fq3YcZ9Mx2wjfvPHY2v2mpbKBEEIIIYQQQgghfaXrEKVP1BhqKZhEDVZ1fOKmPWIks23SzJhGOqIbhVHR2KgU0GYOJ/+wu3OlgDMjSd4MJmdRbiCp+5NU65SG2ZqWcaGbb8oo9dySPs9iw4xcB8O4ReNml8Y6qrT1CjH5Wj+5vyg7NXOg49mcma1xvtMsj5B+Ua1qu5tcJwqk0UzjsBba5cigjoTWTZuuBcVBzcRzZFUIhRGr3jOu5vHszHGKKzGysnEch+CPjRmOC/HcNDHuzT3z2PfGNHKXUUqd3LYJAJDeeH1RduqYrAe+MK8zPcmozgBVY5ndGhpau2Y65NlBcuwEgHbzq/GPi8HasiZzgbYZRGvE2m121cSmK1EHtBkWm+ukJ051nJOv4922bvn2TbpdC2rHnbp/7BNqHue+/AQAIN6yuSg7+dKrAADrHlDzK3fC/B5G5VpLu9d3PhshF4hVA7nHZYY0Gh3V/Q9J+7WmqKVYM/C6KmKzXNljvwXDbGRbPJbMpLZZni8Tp2dfMzKm0Ome7Z3HNcy5X1SjSwQVpHPm2W/ZLdf87INFWdtMaVBBNO7UvpaQfpHNqtGwP3mqY3/eH7bFmzUhztV/K1UfARqHpn+1iw9Y9d+5Yg2YrTzfmrJqYafSr/HC24qixqQ8+/gn1Qjdm3dZZfeucKB+8641qGwghBBCCCGEEEJIX+FgAyGEEEIIIYQQQvqK82t4XU5CCCGEEEIIIYRcflDZQAghhBBCCCGEkL7CwQZCCCGEEEIIIYT0FQ42EEIIIYQQQgghpK9wsIEQQgghhBBCCCF9hYMNhBBCCCGEEEII6SscbCCEEEIIIYQQQkhf4WADIYQQQgghhBBC+goHGwghhBBCCCGEENJXONhACCGEEEIIIYSQvsLBBkIIIYQQQgghhPQVDjYQQgghhBBCCCGkr3CwgRBCCCGEEEIIIX2Fgw2EEEIIIYQQQgjpKxxsIIQQQgghhBBCSF/hYAMhhBBCCCGEEEL6CgcbCCGEEEIIIYQQ0lcq3Xa+66P7fb6dZmn4mRX7q+FnLdIxCxfXiu1m5gAAs83Foiy2wxtLCwCAsaF6UTQ2MgAASBI9bLYVF9uRk2u2kBZlmXd6f7N9rnjvdRuZ3RHu483RJffxnUXO6XE/94pd5185Qgy//Il9GptOgqoCjZOak90t045dqg20KM1Mm69p86yE4iTROEM4P4rNfeKS2MxMM490uxq205YGty/iSI+rVU08R3Fx5NnXAYBmuNb0YlOrGd4Ndacvm9S8F7L8ki393fzcK/cwNklfeGn0upKe4DyINLbgTX+Ut+tMY9PVpQ/1Jrbs/p7k/ZTvUXVTJxd1hoyr6CeFD524t535efDR7P2MTdIX+habFvON50J/eKFtvm+4ZUKnV5yvEMYm6RerEpuWlfZxzxLWWmxS2UAIIYQQQgghhJC+wsEGQgghhBBCCCGE9JWuaRQ+rhbbWS5zNsMTiw2Rii2lRgJtZNm5zLIS6W1cZuVlcjGbBjG/tAQAiJ2mY7hI6xGFlI3IpjFYhWlZekMP8hrbkZfYSESjIMtutVSWahTYep2yWy8nYyPkAljKtLVmoTH62MrDpMyEI5qJNtpKFNIsnB5QN+lKPm/M5vz8HeAbJo1Bw7TIhIjMOTZ1IwlS8KRlyiIpi03KQ9LQ7Uq1BQCoOq1bo6rP0WhITMapSRcJMZeZdIwo0e00/O5KVOCErD4rlXMumwbR2fn4Zkgjcuc5f7BSaampU5HZYfq4NSMfJ+RiYdNv03NIXboYfI1IxgnpCWPhkkJlAyGEEEIIIYQQQvpKV2VDy8yE+jBDaScDo2AWZY/Lspbuz6dFrSukmYGs1cTUKonVIHIhGFwNVvWcqGLqkdfAGFX6UuNGU9OyAS0zG5OFa1kzx8jMEOXX9+ZCZYNkvqSwrIyQC2XJzKBkuYmjs4Zy0u4WjZGqN5OOSVAHVI0cJ/Gds5ZRxcRuONaZNm2VE7lPrC1z5g0T5e8Qc80YnQqL2JrhpXJsalQbVaOMyMJ4qTfGddVY6hkZZUNaseqroHxIS+RJhFwoVs1WYuZ4wTMs5Z1P+HkJZlbP5XnKlH7sI8nljG3TbMuEENIBlQ2EEEIIIYQQQgjpKxxsIIQQQgghhBBCSF/pbhBpjW+6yMOcWyalIV9z2JTZVIVWYxEAUEOzKKtVBgAAagnZTiuYY7UlTpQZvfU8oJPMpGa0bJ3z/d6OzXRKsF3JfSiqI6uByTJCEta2r1g1Z4jsassUVmKz6dt+Au3t3ychfcGcXs8l4fZGNl0ptP+qMXB0mTGPg9w/q9p7BrPGRMtSc80olmtVjJujM3GYRSIbj8x9cufHzFTemrdmIZ8kOw8zWUJ6YvvAC0lrWA15dq++8Hzus9w1u6V7EPJsgW2aEEK6QmUDIYQQQgghhBBC+kp3g0gzL5+bwpXNaUZWrdBSF7o4KBtcpGMaqTGsy30jh4yR2/Cg/EwWFoqyRjSk2zAmeGfVAwB8YS7Xedy50KbqKClbOZw9Jf2nrSVW8mUutc1n4QirtnF1q0CSY1NnVQZ6ySQoCpxRHFRqwXjRLGVrl7pththLve6PjOqpFXxg41T3N8M6mbFZIjONzdKWwW+2bpexNCHVyM0i1Ze2MLfMnL5r7FK5PhjaRhENIskapkd/46pmeejQmdqlJ0uXoVyNWVjO7JJnMzSAJISQC4LKBkIIIYQQQgghhPQVDjYQQgghhBBCCCGkr3Q3iOz0XEOMTtO1yEWl5/gg5a5UjazanB8Hc7pWqnLPpblZAMDc4SNF2YZrby62W2F8JDEK6MxIufP7txnThd3L2NqZ+prtXuaYPdV0+brnlN2R/tNY0ryBVsie8BUNiigJJonGoK5izCKTcKxraexWUr2my4LxojGV9E0xcnV1vU5mhiujkD4RmXjOIm3/cUjJsBExEA5NanrNqonOPI0jaxpJ+IBJqQi5H4kxnYzzmKuZZzMvjKQu+5MWU5zI6uKCeatPjVFk0SFdmDzbJxqvPg1tPTP3iUwqYXYeRpV5/diHka9l+tT+47GxYjudmenLNQkhF048MV5sp1PTl7Amz16obCCEEEIIIYQQQkhf4WADIYQQQgghhBBC+krXNIpD+54ptuPgKl81smpXq8rPWMcs6sYhOwrSzWpD92cVveVAHGSaiUo8Ey/n17fsKsrOLDSK7fmQslGJ9T7eOuoHK3pnxlGifDUMk27RLo2TenibImL2lonoXGGJb6WwJp0knJU5Y5NPSJ84NTdXbOcK7ahqZNOt0O5MipNdxaES2mdspNxVk/JQC+E1HFWLMu9ku2LOic1FmyFVoWFSNxqLmr6QhTiNzWoVWXgF2XD0JmMiT2FqmsLIvE+qIX0iMatZ5CtxpE0tS8yz+ZB30orPQ1pOyDlQrAhhUyZcZ/qOM/2iD/2Ui+wKKiYoytIbfElbtqkT55MSUXZsWeoH3frJ5cIlbL9MnSBkhZT0kasam0ydWHWobCCEEEIIIYQQQkhf6apseOAZNWnMZ04KlQCAaq4yMLP7lUrV7JeRKDvhumQGrDaNi2HOrnVqnLNlQKo0MjRclC0uLRXbLpOLnZnRkajFpu5PwwxQbBQWtVpdHsFoFGIzk9RYEuWEM88RmZG1RjDGS83sUqUqzzk4MGjOMbNT4WfC4RyyChw6errYbraCiiZWFYEL5nEtFQXBhETRfodrGpwDVZ0JHRkM7dqYWg0FY8WqMaIcMG2+lUr53EKzKJuf1dhcCnG6GOs7ouIkNmEUFHHVmrsGU8nMPJs1fw3KBlfT/b4pz+SsJ1/NqiHCLRepOiL9JxoaKrZ9S/qMdoPI0FatCbFVLpx12NnXzEJ/VapmsJzHjK0zL4lcsVhqkAx9NqvAaNufP7M9P9TJ1ex9Ok1o235fhPSJtraWlbXrzthsv0CJKsles6zN97rOpVQDXaiJLCH9wrbFHNsJXixWOzZ7qQzz/UaVXPbNcLnBP4UJIYQQQgghhBDSVzjYQAghhBBCCCGEkL7SNY3CDU/oP3I5s9nfCP9omrLUHhFM3YaMXK2VqnR5eEFk1X6kXpRNrJMqbR01JnQTI8X2yel5AMDe4wtF2ZOn5rXOhaRN97uQzlE38u2qkew0G0vhOK26FcvlaRStltY9TycZaEujMHK6IHuptSmDbgIh/WBmRg0iQxghijXO0iDnzFKVocUVHVusVCQ2k7qWNQdV2lwJ11qcWyzKGqExD5iUhvlIY2J6Ue45s6DnTM9oHsdSS8qTpr4xWnEz1M3Iqk09s/DeaTT0Os2WeaZgXItIZehJUgvPYNK7TCBWKlJeq1E2SvpPtrDQ+6CVYDskm0YU0ha8N33k6KjsG9W+ElXTvYf3QHLgYFEUDWuqog/xZdMXfMv27MvTpnS1UtgyyWf+HWHi+fIVhpLLjZ5Gq4WEeRkpdUmbbkv5yaXPvVKcenGx0izOJ3WizLyPkAulV1vs1e76FScXmgLVo57536jLpgq6Tg3Ais+5FGknK4TKBkIIIYQQQgghhPQVDjYQQgghhBBCCCGkr3RNo2iTOuZrfxuJSFYIIJdbQzxIN51xrzfysigTSdvRaZVdZ6Fs/5RKURuZSjOn5kW2Pb2gcriFVKUsM8EhOzLjKHndK5GVvJiUiHCsMxKUNnWMr4W6mdUm8nsmRnZqpXM+/32BkL7T8toWs5rEaWpWWHFhdYdWU9u5M+HuI2mriZGVJqb9N1NpuAvQ2JudldSNxaae00o0UJpN2Z46reuJz87rOyRZkvMavvOcLFK3/XhEUyqWwpISqXmO1qLe38eSQ5IsmhUqQkpFVNG6V4c03akSSdpWfKFSV0L+F3tvGmNJlp7nfbHcNfesvaqrq6q3mu6emR5yejgkMRyahOShaNkYSaBsgTAgA9YPE5DtnzYEaQjbEAnIgAEbhn9YtE1DECRLpiyRlMBlSM7SPZyZniFnOL0vVd1de2XlevOusfjHWb43OuPeW5kZWZVZ/T5/6tSJiBMnCueLuHXO+73nAZHBt7hMuplu2pjb3NxxbCwgyQztzlB5T3eP8TtpJLBrC0o8y2SvdLQnR4UJaT77ZtouD9Puc5Au+Nj+XtI1jrAjPjnCHJZxt8/YLdt5qnjCzvfF1OyII/BblsoGQgghhBBCCCGEVMpEZUNamIGxK/Wwp3aW7dz7Mwh1/iKwiock0ONzoc7ANO2pKx1VMfRHZkU2XNd2ukO9vmlN3zKYkZ2BNocjuxKaqulkzc6p5KLnZWAel9lpoxwUGIWZpNwZcglcU2YMUpBD7GyHkIrIYfxlkVECBLDqOLKKhBDqklzLqV2tjGvaTgQqhWRkyltg9jgamPIw1XZqmcZRatvvDXUVNgMzRycCGg41DnsD847JIDbDvKn3rEWuIV8nNbinje0EF5Jy85cITPXSLqqnTP/ijMFJDjH7NKoKQOlUtpqS9VXFEDjjxhGcV7oie/hXUAh5aNiYKcTeQXxmwmhn3TRV0UEqOQj5OAPfYm/mOE3B8DGDygZCCCGEEEIIIYRUCicbCCGEEEIIIYQQUikT0yhCSJkISvYfdnUo6UYDSecbGcCcRgpS7kZo9GWdWM3bNq3seqal7cR1bb9h9w7f6KmseqamkrLZujl+dU33CO/a+9cgdQL75JXmhf2WtVjmg6mX6DU5zbHIAyJvQHrE0MQMmqKmdZs61NfYCEVjIrIpQ4lmF0gW6/jtd8zxZl1TIgZ2fMeR3qdem9H26+Zes6kaPIYZmEHae67f29Z+ipFyD3sdfTZBo0sbdCHUCcaxSbuSttZFuZGvRRCOGZhKpql56DynzI08BMpcg3chZw4aJkUQDZydbHuadDNsqxEr7tnt0iei+Vlfl3W276vN6R3egwkdIQ8DN1Z3M05Lzj1wCfWDStdA6HZOSDmF/wfa8jST2I8ZVDYQQgghhBBCCCGkUiYqG3ApPy+ZNs3LZnSLLooiIpLCjGg/BcO4zoq5JljwdbWGWVk5Na+roy1YSb1w/LiIiFw6qSs0M009Htnmv/HOLV/3J2+b+6wOYfVTdqoxEjDIK4gcSma783znLG9W5hnJyWByACRgzJinRrGQiJqiZh2j/MkinV3NYRvLyG5tGYC5ahyowqjRMoO52dA4OzZnVARnF4/5utlZvWejYYwdwxDeFQMNioHdMvPDlXVf9941E5sra6psSEqmQIfwDgAvSUlsHIYBPJuN7UhfIZLHYKi55ba6nfL6I2S/TPoA4DaSAjFTaj4M8T408Y6rmm7rStzOUuB4unJvRzvhon53xSoa0nff11vad8c0o8mpUM1Ajgp7GasFNa/9tqAq2KmO4LdvUK/5cua2m8XVT1gV9YZzI1Um+u4+SBO6su0yCTkiBDXzg7CwlXO+U4WAsbun+HJxzDgpQGUDIYQQQgghhBBCKoWTDYQQQgghhBBCCKmUiTriUapyEzcrEYL0syyNoswMKkcFNNyxJkY6/eKiSrFf+OyLIiJycl5PzKCBupW7nD+hMrQQ5GdJYo7Hl0/5us2eOf5776p8O4f9wgMrb4tBip2H8Jw+jQKkrqmR16Rwb5y5yV2aRkm6BSH7BeVdqRvLaVeP2xSoHFIawgxknIGRlAVQl0BMRKGJr7mW5iKcXDBS68eW1RSyPatpFvPWgDWAdI14Tt8RfWv+OtvU2M1tTkSWaD/W4SURDM31KDtNQm2z2bf/Dnnf19VsPlOjre2M6vA+sc+edzeFkAPlfqXH076lJWB6QzA/Z5rZ3PJ12VYHTrbpRstLWgexL9YgMj51wlclN00qYnzmtK9LV1Z3dr1MljoNmmdJyf01AAAgAElEQVSRR4USY/GC+arLr4Xfj2UpEUWzR0jZdW09jJgpM3dlWhQ5goQtk+abDSBNwn73MJ4qM1plnBSgsoEQQgghhBBCCCGVwskGQgghhBBCCCGEVMrENAp0z3WpBHm4UxqSF/YYBam2k3KD03YUN7U8d9Gc19Y5j8H2hoiIrMYq1Z5r6zVv3zXS5+++oSkR2/du+HL79CUREQlTlcqMukbmOQuS8j66+Qfmn6EgTMtVGpq6Z4LnzKx0NIPnjSNwIHbN0PGeHADBCFzlA5NiEMTgcO1kYUONrRh2dAgD62hfg3geaTpTd3MgIiItiPdBy8g4+23dtQK3YOnae0aQgtQAaag0bJ9HGmnNzJwbjzQtJBuAxHRgpaMjTREJA3Dhz00/o1QlpjV7m1quzzPA9u27Ydh7gE7e5ONNcXsjW7cLvSZcH7ZN6lIwM7PztG2Nk6CuKVD5wMRJeueur4tOHNfj6+a7m25C6oWVbee9HtygpM/jdtVw9SUu+yG68feZRkEeEcrSG8qyocrSE2DnqPi0xma2Zn7r+l0r8PpxUu1pxydR1rdxxwk5IqSbJWmz9zmWMcUpOq67sSV3zG5qhe/iQaZPHOHYpLKBEEIIIYQQQgghlTJx2T2CKVmnXsCVfFdXUDZAOSiZXQ0yXdH4sGvKb2yoiuC1ex+KiMjC8pyvy1K9fn3DrLKMrr2mD7F21Ze//MtG2XD3uqodnlwwK0BhU9t8+f01fU7b/AKYyM01dJa5YVeIAph5HgxNn3tdXfXZgBWauwMqGsjBkeGkph2rKcarU+4kqhJIh7DqYhc9hwUDU535jTPT5mpfV0rjjmkrB0VABKaq2yu3TSt9jeelGVUXzB4z5nRtWHFd2zArqfVM42gRzLXCRaNqatcgHhc0jsN43jwmzJvGNbPyu53oStCNDX225I5ZKUqyqpyACNkF97vyMea8rGtiMgC1Tn7PGDeGTY23AJRM0Qlj/Jh3NZ4L7btvG6zMxucfM1V3V7TNhrbvr0czPOjTJBO7zCotCHlo7Gf1fzdt7sXMsQZmkS5WYPXU/RZ1KicRkXRLzWH39ExWdYS/c/dk/krIUeF+xzRujAC/P+X2nfGX1EBZuM84QmWFtnm0lLlUNhBCCCGEEEIIIaRSONlACCGEEEIIIYSQSpmcRoGGiGLK9UgvSaysawByjrwgEXH78uqcRgA2jAMr9b7XV3lY3ZoszvW3fV0KapHZvpF09nOVRY9AXpas3RQRkVsfvgn9NA381M/9gq873lLTyZOzJp3j/DGVx7TAOK/ZMHKYGKQszjQyATnolVtqWvmPv3lVRERu0vyKHABRE2SWIzNWQzBmzDIj20ohVSDpg/GiNZhMAz2exppSMRCT1lAXlVQ608nOAAzjwDzugysm5tZu3NR+1vX6Y2fOiYjIqRPnfN3yKWO2s/z0GV+3tKAGlCfnTOrF8nE1wzsO8rSeTa/IEn2OgX0f3FxTs7tXrqrc9LWhidm1wdGSoZEjTKhx4OXQ+5Qlh4sL+hcbm+nahq8KhhrveWZiAY0Zk5u3fXnrlz4nIiJz//xPfV1685aIiETnNDaTD67t7EiZ+WWho/rsTqIdnTrt60YXTujxl/58fDuE7JdpBmsPASeRDqBvyZX3fTl+4qKpe++qXmPTmQrpSLsxjCs7175D8hJDV3vC+GsJ2S9lY/WgU5ymXWLjDNMD0zff8eXo2LKIiGSQpuu+y+mq/n9w6j2nPPt9p0wc4tiksoEQQgghhBBCCCGVwskGQgghhBBCCCGEVMrENIo6OOIGoZFWLbTUjbqbGMlGb1NdcHH2okzRUQeH7Ny658eQBvH4vGn/uVOLvm51TeUoG1vGTXsEUq87sDf4n3ztayIi8skXf8rXNRrmOZZmVUp9/pRKN0/YNIrFtj5bCPLydtPItkPo+9DuRrHeUXfvNz/UHTDSkXHCDzKUoRFSDY0QZF2pGaup6PjObbxmkDkR4l7BNs2iHmqQNqBcD0ya0cwCpBstzZq6WR3Ta7DzRJzXbX80JvIByMNCE6en5lXK/dyzRqL9OOxd3Ag1TWJu1vS5CbtRRDW93u3KkSTa9+2h+feoQd/ebsGzDVJ7H6ZRkAPGyiOjWU0Dynrm25CPhqWXfPRac/LOj2kwo+lGWcl+3zmqoa0cNICdYKSvu7Vg+oS/ZsHs9JLe0HQLdOUuddnHfjp3e/wd4d5BIAutvXfLlxP3zAHXQcgBcNAyY9c+ph9M241iyljHlAp/iY1j9y6Z2p+x994p3y7uRsFvJHlAPKjY3M0lNhUxiPU3J6YZpXYXqMI1HWsBsJtdaA5x+kNV8ItOCCGEEEIIIYSQSpmobJiZ0ZXSKDIzL6sba76uOzR1aYqrGWAG6Q05dLUlxFmhzMya/vhjqmL44tPWcAPM2zagl2liZpq6W2qENTuvRlkvfPZFERF58Se/oMetYmE40JWkECd0c7eaolV12E98NDIrpNeuqjnW11/5gYiIvHJTVR2vr+uzbQzNSlYY0+iKVE+U6OqDW0QJIo2zYd9UNiKNzajgUWfGNC6qNBpqkHr8pIn9s6BsONYygRjDvQezqmJYOGlWQmvh477upF0dFRG59PQFERG5/PTTvu7xc0ZhtNTSVdoElmSjwPY/1Wcb5apY6G6bc69vqVFlZ9uYZl1dvefrbt3Q/ZB7K8bMZxioCS0hB4FTFKSbm1POLAGDE2IibJvYTK+rIsB/0MYoDzKnYgBDufiMmjQmN6EtS7pqvvXRSVUBCqykplv221eiZhBRM0o0jXYqh+zkkp7X0Tbdv1fWn7JiS8h+2YNh3NQmrXlxnsKq5pT7OIVTnoJ6b16/m/7dASqEdM3EZgBqRQnBNHqaIqGkT5FVFwZN/ebnoFre0zuMkP2wG4XQNPYS7/ZcjGf3/RURybrm928ApuWVfbsg3uOzRgGcD/W3bwbxmNvveuF9cMigsoEQQgghhBBCCCGVwskGQgghhBBCCCGEVMpEzcUmyDTSkZExDyHXILcpE/UxreRiJCg4o+Fl0SLy1CmTavDLP/u8r9vYNhKUtQ01hVxq6A2ud0z6xKc/+Zyv+/wXfl7PXTbyzBYYejSs7HppXuVhTeh03RrF3Vu56+tefeNNX/7Gt4x51kvfeMnXrcUm9WP5p/+qr+smYFwXWNlNRoMdUj1pV01RE2vsWAM3yKZNn8hrIMVKQN5ly3Go17RE4+PcrDGDvLisqRXHaiaSE9ExHSR6TeOMkVsff/Ksr3vs7ClfPjNv2mrPgVzUdm+YqjxsNNKUiY6VpN29oSlMH9zUZ3/rh1dEROS162/7ujQwKRlhQ00ne5HK3Ho2rSuO9inLI2QK9y2pLNtne1ybPZMyFD2r6UjpG+/ag+VjOloy38Uc0ijKUicKWAlpeltTkLCf0Zx9N0BOYtbR1CT37OGMmmMG9r2Sv3VVz4PUy8zJREMaK5MD5gBM2fLEjN8Q0gJdvI7DpS/kXT2vkLIwQf49Nl2ixJwVY9+1FS0uwHHzWyBdXYfz9Ft8EGknhExkL6kTU4yVd5Oa4b6bLl1C5CPx7NrCOJnWJ5vqWDBiBbNolwoRYmxumd+8aWdM6q9tP88Ob2xS2UAIIYQQQgghhJBKmahsGIIpRu5WA8HwMLDLkrjFVgLzF3U325LoCadmdYXxr/3EEyIi8tii1nXtNpanFnVFdamhM0DHZ8yWls9eftbXzS8sa5+HZva2AauWoVU2rN656evev/quL3/nle+LiMh3v/8DX/fOu+/58lbHzDKnov1Y+vyXRUSkl+rKbpDo7FTNbZOZcz6HVE9/Q80PE2vQGrR0fLYiG9oqtpE6xGGQmxnSYKgnHJvXOLx0zCh3TkMcLjbMO6AuGdTp+I5Ds0JzEtQQy7BVbhSbfm6DmePmmtmy7zaoMq5eUVOq1974kYiIvPKtb/o6NHvsbJqth4ZDiL1ZY1C5fFEVU/XTYHInZsY4qlPZQA4Hha217Hc3AMUALpzEp41aKKuVrNCMWdVxioOx222WrFq6FZaC2R1QZhiHBlWujNvzZdvXx9+bkIOmbKxVaRAJcXy/7WfrRq07VqXgri9ZHR27MmtfGAU1Q8mzp+tqtO5WcaMTqghM76ja198z53eTHADTvgNlcQTX+G2d8f+tGFPu+l2M38yaIBfaKVFG5PB/Q//dnGLSWvgWQ5vue5uu6G98F5vxWTV1Tu+u6DVui87w8H5L+T9hQgghhBBCCCGEVAonGwghhBBCCCGEEFIpE9MoAkHZipGEBLle8tqv/y0REfkOyFv+y//+t/UKKyOJRipbeWxW5zcunzHSkF4fzDFSI/uaaaqp1IVLF3w5fOKciIg06irPTocqy95aMaZX33vnHV/36quviojIn/1A0yTefQ/SJLZsmgTIXjKQ4livPWkeU7O7uROmHyiVycAMUmU1U4xDCNkDrRlN35F7xrymXdOxNhuZmGo1dU/gKNLYrdkUqKUZPf78JU01+ORZY+Q2A2kQM/byDN4LJ5ZUNhrbfb7btXIp9827H4iIyA9fV/PVW29/KCIiH95VuebdO2pc99YVc8366qqvSwf6nJk1x4zamkrVOHPR9PeJJ7Vvs7pveR4auV3W577h5AAoS2WAusEvvCgiIv/d//q/+7p/+OznfTlyY9WmMomIyB2VVDrJZPbDN/QaZ2S1pSlI+G0amz7x0X5ilTWbik5CCtLxJT1+xcQuGmYVUi4myMfL0i3MJfaaQ2x0RY4wU9KE3v9Vk6abPaNGbJf+lv5u1ItBvl2SOoGGckHDfENzSPXDfkyTW5dd46TgRfNVLWfW5LEQ91PSOVw/srU1rSy8y/hblhwgOD5LUire+6efERGRM/9c031bv/09vXxk4whTi6aYQU5LeSitL0tdgtjIE5vWXNN+Bk39HZ1t67thYpuIfUcl12/APXdhGHsIoLKBEEIIIYQQQgghlcLJBkIIIYQQQgghhFTKxDSKRgTyMKtGeebsSV/1qpVxBCB5+fX/T2UtG9YBu56oG/XcSCVaw76RhgwGKv2YmzOy7nZD5d0BqLdmrHx8bU0d6f/4j7/hyy+//G0REXn9Dd1tYuWeuecwUWfeNINGUydHUVkKSs6juulL7djj2idbF4KLfhChHNS6AeeHV9ZCji6Xn9TUoq1zZlyfamgcHpsz+3zPNkHuCaqrpG/G59yMzjc+fVb39V1cMLE/Vwe5sw3ELAUJaAYyNZtqhbtNXLmhsq8//sPfERGRl77zHV+3ecvIw3sjjccU4rDbMZKzFN34w1lfjuZMn9tnn/F1S099WkREZk+oc++wp8+5PbQpYVSFkgMgvqjfiXzdpOpgesOf/IZJn7gy6uh54Bqf2nIA10SnNYXPSykDHdNOoj12n+2S3Sbic2f1+r75Rqdr6k7v9gHP7mkKk0B5T5JN24+wrd93lJf7csB1EFI9mHaQD0e2AL8/3zfx8d2/85u+7kvymYltRsc0tSi5ddsUUAbu0jR2seuFS4sSEcltmlKGO0vYtrJtTfcQLO8B944qpDUdYlk2ecTAlAeXlgAx8/RXzLf0337tt3zdl/7Nj8E1O+MrWtD02dSlB0FsjttpyVPy3cRvl0+XKktpgBSmqWmMU+7pd34q7EhTnq58WOEXnRBCCCGEEEIIIZUyUdnws59+2pcX22bm5MkTOlP0W19/S0RE/vClV33daDTy5d6MWR1NYMZ10IX5jdCWYcm1XTd1tVDrOiu6Otq5YWZ4vvrtP/N1/+Rf/q4vr9h9gVG4kNk5lSzQmbMw137m1sQxqKmJRx2UFfW6eY745DltNLYGfWDskYnOPHu1B/ckJgfAF54+78udvhl3cQgGkUbYIDGYs6ZDXaUIGmYlM471mlFH43TYNON71ITxbZ1SV9Z1xbXT0zgabpvYvHdNzVe/88Pva/lbL5njd29rn6y6SWr6XsgEzHRyE7NhAxQWdY3NWsus+NZmWr6uPzL/Hnfe01XY0TyspGZmpSim4RU5CBKIuXVj1Ob3ABeRX7jwEyIiEoKhWzQPcdYzsZknGlsFYyi/3z0YpdpvbMGUqq7KRGdQ5doWERldVJVi9Gdv2YZgj/JpplV7wa7A+JWasefxu0mqBxVEftUeVlSX/49viYjIl/5PWDEt27oeVx1X7sG5O1cl/X3GrUSWmOEFC3Pa/sbm+GuqXNG0bU5d7SXkIJjyvUnfNr8rv3QWlUZTTE/BvFgrS64ZF5vTznVV8N2V0B6HONqNQsgZzhbUEO4ddRDf5AcElQ2EEEIIIYQQQgipFE42EEIIIYQQQgghpFImplH8zc9d8uV6w8hJ3r9519e9/DVjzPj8SZUwo5xkaNMj3n3zR77uqafVyC20hnLr19XMcdsaVN26qQaQb7+rxz+0krWkreZvy+e0n3lk5KIoGU/slMoAZClJV6XgrZo1rQLpZr+rkvK0edyct6Sy0zw1EtcEZaeiZZdGkaY02CHVs7ioqQbziYm5AY7Znimvbahcee2OphVEgZGXNWr6CmjPaYpU1jfmdXdDnY/csHt3//Btjcf1uyu+POia2N0erPu67pbKVqOmaX9hWaVpmVWcNRpNX9eP1QByODBx5sxkRcT5UIqISGil4mFtEa4xf+YZSGY3VZKe2phtxlpHSFXkkI7kTBiTG7eg7oyIiKQ3tS48tuzLgZNu9mDvbpBkxo+bdL7k/Q/1plZmWTCru6OxKX1Nn/D3+dZf+HI2QZ4ZNPRds1dpKCGHATRQDW2qIBov+njFtCU0rnNVDU1RyiC2ohMnREQkvXt3xzUF01NME3LxDvLs5OoHE55CSuXdezJ2LBjOlaw9Yj8PInWDEEdJegKOtej4MRERydbVxLhsnEfzYAoJJsvhnElNQrPmsvuUUTQ0ht+Nrs/hzr7vKh0JTStd+mQhtePop/xS2UAIIYQQQgghhJBK4WQDIYQQQgghhBBCKmViGkUv18Or20Yq9sZNlaC89KPXRETkWlslHsdmNaVioWYkLvNz6qzbmlvw5Ws3jczz7ffVzfd7f27c69++pjK2rT5ISGIj6fz5H3vOV/3is0/4ctNOnzTrKv28fsekZFwDWelmR11K33rVpHm8+b2XfV0GEpj6GbMrRxZpm2nXStJxhwt0AvdpFEfXPZQcXtoNnSfsJyY9aKWn8u13X3tTRERuXn3f1wX9NV9emDFpB5cuQ+w01B2/bndv6HdVYrreM/dZv6m7SWxsa0zV58z1n/nMv+/rnrzwmC/PnDBS8STV98rKtknXWFnR98rVW9d8+e0fvSMiIh+88Yav69oUDxGRMDLxN9wCaVvLxFwQYeqFStKcTD3POddKqsftQCEiIq4MEuX0lIm9/EMd5ygNdd+OgkS0RGKN0s7goo2zTUjhOHVCr28ZyXj6gd6zIPOc5HbN1AnyqIC7h7ms2ik7Q5Rdnw3LD7v0CYzNcNmkNqW3IbUCZNdup5qs2y1vtCx9wcZrEKL8uiQ2MQUEpdj2fRS2NH0xg13jdtybkIOmkGZUkjZgf+thKlTx+pLdVCBmXPoEphu51AzcUaYsjsbGprsGUrFcP4JIY6+szUJ6IjyT638AKc6F9o8o/LVNCCGEEEIIIYSQSpmobPjTG7oSOuibmZWbt3UF0k3eroLZ4pVbaux4ds4Yvf31L/+Mr3vuUy/4cr1lFA/Hzpz3dSc/cVlERH4ODB5PLqsaYrFlurzQ0pnjRlNnZ2dsuQbGdh07K7Ta1enom+tq6vP1E8YAsgezSzfuwUxXZOq7q6q2SO2Eb6utZnY5zCL71Sma6ZADIEx0XA16ZlX/1gdqOPfq918REZHNa2rmONNSE8WzP2ZWWy4+c9HXPXP2jJ47Y1dCYfg+dv5xERE5d/6sr0tHuhoyN2uMeS4+8aSvW1pUtUSrbmIX56y3rPHjjY6qFd64ckGfTYwa4u51VS50t1/z5b5VXOUNnXkOt028h5HeKcQ4rdnYT/W9QUhVRJ94ypeDdfNtTFf1W5q/YpR0BUO3oX6b3EpowdSx5DuCqy3RDaM2SsAQFldW3KrROLXEpP27caUonNF4diuyKdyz0A4N5chhY8qYd8aQOM5xxd8pFrL+5JVGjE1/LtyvYDg3Tf1aFj+2rcICcImKAZUPEoHy1r57st5O49ip9ybkICj7BkG8prfN/y0L300Y/6FVCkwb0/gNTGybOM4x9p0ZZD4aI2UqvUG+4z5Fs0d7vEQNISISxLXxx49wPFLZQAghhBBCCCGEkErhZAMhhBBCCCGEEEIqZWIaxRpIP50iJEhVzlwPjCxrGKrRxelllXk89tRnRETkiRc+5+vmFtUsMrSpDvOzKiE5deyybVv7EYJWJpDc/gnyGpSWpEZ6MkxAQm1NHNt13Rv51II++udffFFERBqzKjP/nT/6qi9/cMOY7KWZmkomNSPBDiNtMxaVqYXOwIcGO+QA6A91fK/aVIbutqYi9AdGvj0MNF7Pnl7W8rMmzi6cOu3rliBdqWZjJQfTnpk5I3M7saAysxBMFuszdvzHGge1COLUtjVKMZ7N8dmmpjk8d0HbDIJnRUQk66pE9Hvf0pjb6hv5+Ej0HRSF5ngEBrdZT/8dwoGVkOpjEFIZyaJKpOO7xkgY0yS83BnTHFByOc4AawKpM5gEKXXBSCsv27v7Pu+DJlsgKQ+szDM+q++Q5Iamck1KzSDkYeBSf0Q+EpPuuIvJcMo63G7GtjsXYi/r6W/JyqTRJX0KZ0ESDqkfpWaQhDxMSr5NhVRAdxoYK2KqQmZTHnYVmy72MDYPIjbKYnzMt9inbMBxnyIyBCP0I/Z9pbKBEEIIIYQQQgghlTJR2XAGVjBH1sRmFOjqf8Nun/cB+FjUF4778s988bMiIrI8p6uWI1AcZLlpswMmH/XYzH/M6QR0scO5me0JI50nidAEx63EwqxPnpWYNUJxcd6oLS4/ecnXvfammuVdv26UDQm0GdkVpDzH2SkoZtlHqwipjDzTGd2mdSutQ7yePG22tDy5rGqGZy5f9uWzj5ut8hpNXYUVUCHkmRnfAxjBoZi6Wl23t223IA4j9zrRdgZgZDmyZpDDUOsCW6yBeuk4GL4+f+6UaeeL+kLoiK5Ivf6qMYvs9LQu75n7pLHGawy2lLlVYKQDRiepnvj1q76cdewqCSiEIrvamG6qsWJ06qQvOyOssSoEp15Adyx3fK+rHe5euP1YWVuonLC/CcaqGR4BUyvyaFG6hVy4c4u6fEtNz9HM0Rs/7lMh9KBIYUvdAvcbm3t5TkL2QtkWtPA9civ+qGYoqBz2sz3kwxjbcE80vQxa5vc1Kq9ypwY+YmoGhMoGQgghhBBCCCGEVAonGwghhBBCCCGEEFIpE9Monjg+78tpZiQd67FKN7sLJo3i6aUlX/fkZ1/w5XPnHhcRkeFITS0ilGrvKIhk1tQqz1XaFmPKhJ0fKewfDA34VImyrYkzSOEA86xGbO4131b59lOPP+7L7773noiIXFtV2WseW4PIQM3q0AwytP3L92D2Rcg0INNAMpuLcGxBDR5nXnxeRETmZjSF6eQpPb54zJSDusbWINXyyKY4FTzsbJpEE0IvhfnKsMRUK4VzezaVobA1uAuPTF9FSaBSsZkZI5N76qS+i56+cN6Xr99eN22/856vG9q+y1ANM0ctlds17LulMc+5VlI9KF0OXaCCZNKlT4Rzapac3r3ny05SWdinGykxnNsTuLd3fWfeYu5MaMdITJ1519h+7keaGu40BiOkSrwEG36jBU1TF7T0A5veW9158aOYUmDfByFK0wvm6+a9MzbeCdkP+D2KazsP2+8ifqt8WhNef8Rj03//wUTWpSweZfhrmxBCCCGEEEIIIZXCyQZCCCGEEEIIIYRUysQ0iuNz6jo/GppTO12VULU/aXabOA/pFpefOOHLdTuXEdb0NjVQftasUjIGxWRgnezjAFzwcbMJW0bJdhiii75NwxDYjcJ2eZRr33O4JrIu+zMgtf70p571ZefI//vffMXX3dno275BO+jkbZ8j2K/UlZASFhoam4N5M9bnQP5YO23icHlZnbRbML5bVgIdN2tQp3GahqbNMNLjTRszNUhrCqOdqUMRqNgS+IvdaEYGIHNLbJhGsENFCDu8hLEpn1nUvl+6rDvFXFtdExGRzsqKr4sGJjYlgLQpeNOFdgeNqKUydkIOAuci7ffOFpH43FkREck21fG+uHvS/Ta+T7koXp+5fc3B/dt9z3LtWzF90RBCThfKrkMrRR/riE/Iw2RCWkA+3NxRd6SYJikviW13buHfA3cDYPoEeVDYj2AhfaBsfBauObrpE/hM6dra+BPH/X/SPfsh/v8mlQ2EEEIIIYQQQgiplInKhjzRfUv7dg/TVk3nJ55/ypgonl3SlY1WqDNRbtUzApUCGjeGdjYGDzulQACzVDj5moXOQBJXR7VPqZ0JG6V6fNsaXXX6+jy9gS4fpbn5Z+gleqMUVnTPPHZBRESOLV31dfc2P9zxbAEsSQV+dfbwzjSRo0sMRq1LVr2QgNFV3cbJbEvNdOowputWcdCIwXw10jZjZ8QK4zuy56KaB18gYdmSLMSpUyslKa6Umj+HOIEdYuxbo1VQKl2q613vnTEKjuS5y75u9fZNERHpbutq8mhOr6mH5t+rPXO/S8iE7A2naMB9tNPbd8yx3awUHvB+97qHecl9wKzR7QFuumS/1W2tS1fU6DLdMKvDQU3fQdEpE6/Z2rreZqgG0oFVQWKbhBwEPv4gtlCBdCSYYqqHMqnS903J9WP/DVy8l5jJErJv8P98VNEUFINilfwZmEYWfgccAUNlKhsIIYQQQgghhBBSKZxsIIQQQgghhBBCSKUE+RE21SCEEEIIIYQQQsjhg8oGQgghhBBCCCGEVAonGwghhBBCCCGEEFIpnGwghBBCCCGEEEJIpXCygRBCCCGEEEIIIZXCyQZCCCGEEEIIIcoKwNkAACAASURBVIRUCicbCCGEEEIIIYQQUimcbCCEEEIIIYQQQkilcLKBEEIIIYQQQgghlcLJBkIIIYQQQgghhFQKJxsIIYQQQgghhBBSKZxsIIQQQgghhBBCSKVwsoEQQgghhBBCCCGVwskGQgghhBBCCCGEVAonGwghhBBCCCGEEFIpnGwghBBCCCGEEEJIpcSTDv7aH1zNXTnNUvtn5o/X7J/1UOcsgqjuy8MsEBGRrWHP10U4vdHviojIfLvhq+ZnmyIikiR62tYo8uUwMG2OJPV1WR7o/aG8W/I817JkeMDeJ4ezS+6T76wKAj3vK3/l4t47RwjwP3z1isZmaMI4DjRO6oE5PMJBmWjMpHZ45xkcr+vwjO34TlO9RlJzbhTpfWoQ+z42MxjmEO91ezyDNv3dIW7r0I8wjD56psQQU0P7otjs6QsjDUz7dYjRBNrPXJ8SjfFf/cUnGZukEv5y+EslX4KDJYjNOyDHeM130Q0XU9OugdiTYOdaRQDvhjwZ7b4fJfxB9i8Ym6QSpsbm/cbBONz3KoM43G+beyGYEjIV9YWxSariwL+bZbH5CHPYYpPKBkIIIYQQQgghhFQKJxsIIYQQQgghhBBSKRPTKPKo5suZkyTD9ERvYKTL/RQk0CDLDkJTH4d6myCD/AjbGKZBbPf7IiISBZqOEYTaj9DKtkNMY4CMh6AsvWEKrsc48xKFIOW2KRujkcpvRnBP307ZrafJ2QjZAz2IubRvxmWrprEXhqaMaRK9oY7fKDADeAiDvpWoBDqwEukQxrl7B6QDGNMapj6zKMB3AIR7kpvGklQbdY8RQZwk0Km4ZqTYNZBsp/DWGgzMM4UoH7dNpfrakBD6kdo8iqAs74mQ/YLv/Acknc5d3uFevzf32088L98pR80/JhJV8ohyEPH6INMnHuY9CTnM5CX/aSMPDCobCCGEEEIIIYQQUikTlQ0jMFDLrTkcrps48zY8L8tGetytHKIrJKxq1uvGGDKJ1CCyOzIrNK0aGM/F0A/XAzCqzEuNG3F1SXYCK0CZbQvNHENYSXXt59BQ2cRxXlJZVkfIfumCg6qLzRDM2axwQfpgpAr+kBJG1kQR5Dg5xExmYzqAOHTDP4QxHeRoDmtPA4PIHNQWbiE0xDnOEiPLCGJbEqt+isAgUl8xktm2cojXWmyVC6BsSCJQX9lnzhLOdJMDoGCc6JxYH9B3gN8bQh4sqCaisoeQwwm/jQ8VKhsIIYQQQgghhBBSKZxsIIQQQgghhBBCSKVMNojM89LyRwmCMSkNVtaNdZiqMBr0RESkLkNfV4+bIiICCugCIytLLSROlHliTT1hJxnIt0fYZ3c8L5HHFm6z8z4U7pCDIA51ZGU2RQBHZxqbukBDS2ohGC9aA9R6DVIeMDXJOjdmkIZRs0avgWZrSB5gSoW5plbTawJIqXDy8gxSKzJr1hihaSSkc0Q2hSqGvksGKU6huVeE7qx+O2VM14DLrdQ13YOZLCFTQSn1x8EgeNwzUrZKPg5wnBNCyESobCCEEEIIIYQQQkilTDaIFFy13GkQ6cohqhVGalwXWWVDAKuSKayUOt/INqyuzrTMn0m36+sGYVvLAsuqH+mHiEjutzfZed5uKKg6Surun4/ByhZ54OBITO3+lAms7qdepQAxDIqD3MZHMmYL2dQqBjLY4zW021yGsCVuA7a5HNoG0lxfKyEoHxJ3PexdObKqqBDUChkYwnaHpn+NOpq3aj8HQ3suLCa7V0AK+27ilriZ3cI2CmkQSY4uQbzz841b3T4wszqu7JJHmYewlS0hhDxKUNlACCGEEEIIIYSQSuFkAyGEEEIIIYQQQiplskEkqMecdDkCObI7HsK+4nhNbiXccQ1k1Wj+Fpnjo1Tlzv3OloiIdG7c9HXHn/mkL4/s/EgCCugMpKPu/mhM55RvZSkgCArkpppjTlXT5cWbE1Ih2Wigf3HpSnUda5E1eExl5OuCBOI0siaJYKJYy/FcE2AZSEjToU0/gLos0usDmz4RZhrPaCAZDa1BJNQ17KkZpknAHGgQ2HSOIbTZgHNtzkQS51BnDTPr2k4EL4yR8aCVZMQUJ3J0yVM0orRjfZw5Jb9DhOyNimInnJvz5Wxrq5I2CSH7h7F58FDZQAghhBBCCCGEkErhZAMhhBBCCCGEEEIqZWIaxfUrH/hyZF3ja7Hu8hDUjSt9AFLqRq3uy6GVdNYG6DSvt2xGVuaZqPQzyc31jdMXfd1aVyXj21YuGkd6H5RqZ1ZWHcA8Suh2w0Cn7oI0zvQjxxQROFomogu8JT5KVUGmbq/KgpEQUjWb2x1fjgITk8M6hPOwLyIiaQK7UcB2E24jhkZN47kFl7sUhEaAaRINERGpRTrmY4j91KYqDHO9z2Cg5cy+Q6JM75m5V9A4E30bp8Nc0yjCvt7fZWglKT6nrRtCakUIaVE2nSSJHpBbP/n4cpDpC9h2XjKW8bhLqWA6Bfk4E07ZpewAd3ChPJuQipmWKnifqYSMzYOHygZCCCGEEEIIIYRUykRlw/c/UJNGt3LiVQIiUnMqA1jdj+MaHDczSbB4KrAoKScX5kVE5OLyvK873TRdmm3P+Lpev+/LgV0VXdvc0ONDPZ4mZgU0AoVFvW5WZHNYPo1AYTHoG+VEAM8RwozYYDgstC0iEtfMc7aaLbhG23R3SjidQw6AGzdu+3JuFTt5CCaK/Z6IiIy6Q1/XqEGc2viYqWuczLQ1UOfmTEyGbY3NWWtA2axpHNUxTlKjXNgCJVJnU2NzMDT1fXxHiInNINLYCfSwn4zOwUQ2RPNX26eghsoFJ3fQdjIwlazZf4eoR9URqZ6w3d5RVzBztOUcvieFFRhfBwanCxqHec/EdjYAk9hpioX7VTTAyq9T7+WgCFRF30eeSU/Q8oRV4gC+v3iNb/MAV5jJxxccv2WAKO/+QbWEa2BavB0W81bsO2OOHAUwdvB7M03dt5f2DyI2p6kMy34LHGR/HhD8rzAhhBBCCCGEEEIqhZMNhBBCCCGEEEIIqZSJaRTBzKL+xco3UMQxsH/5L/6rL/m6/3x+pwTk34L047/91d/y5ZmukVjnsw1ft7hsunRmTtuJFmd9eWVjW0RE3r3T9XXv3NvWPkdOFqbHA5vO0YhAvg3yseGgb8/TPuNTuDSK0Uhl1y6dpFlIo9A2cyunqxf8iJ4XQqpgfXXNlwdDI8eOQ5WRpXZMS6q60EZDw71WM2M5b+qYznOVfzeteWI/UyPKxrIZzBmYNW5DbG/2zDXrnZ6v29hQqfcgMfXpSPuRRia2IkitQMPZ1ErjXAyKiIxG+kxR4FJI9DnS1LxPIpDMRg1I/7Lt1+uUjZLqyXo6/ifJHm//3Z/25bO/f0cvsXF6/S8t+7rHfvMNXw5c6hOmF9q0qKDZgPMgH8mS3lvdcY2ISJ7Y+AEdeZ7s7Pt0mfn96dALKSSEPCAK4879BoRB7dJ7Or/zuK+78e4Jvb5trq+39Xtz8T/5C23TybrLJN3jKJNVP6g0i73kjUySeROyR/B7JPa3WwBjzX337vzH+v+oez+lcVifMb8RT/3Tpq9r/evv7L4j+02BmhYfPvVjSuyhObtLaSxLXcRz95QH9mCgsoEQQgghhBBCCCGVwskGQgghhBBCCCGEVMrENIoc3K6dIzXKWjKbVPF3f+V/9nXfBVnJE/bPY3DN5//B/+PLYWYkabc2VHaa2bqr65oGMcg0F2F928hmNroqh+umIOUemfoQ5lFc3+MQJS+QEmHPDVBCiqfmdds32G3C3TNJ4RqQuOTu30sIqZw017EYxmbM5yChnp03qUd5pANwNtaUn4aVcNUzSEmIddCPxEjSOiDlCjc2RUSkO9RxPsz1eM+qujfu6Z7Fmx19h6Q23WMESUqjoblnFmrfolmVf/fE3CtLII2iB7tuBHbXjT7Eu00B0ZQqkVpbpXVxaMpRTik3OQCmyDCdVPvcb1/zda/9vVO+/Hc+/3UREfl3X/n39KIRjHmXUgSpgPloWPjzvroJksyw1bLXJzuPozRz2m4TR9gtm3zMKBm/Ls1i4T/T3583/huQcm+ab9OPPXPF160FJWt203Z52O/uMWU/LB9U7DHGyQHgU/lE/HemMNJs2uCx39DUiOUffVKP29T2S/+TpjVd/deVd3P/sXu/KVZw3tTsiN2kbT0kqGwghBBCCCGEEEJIpUxUNqQFEye7Ug+ma5ldFe1decfXfQ5nXH/mvzZXwkzPF7/yz3y5aac6Vjo6i9wfmZnjcF3nQbpDvb5pV2ozuM8MGOMNR6bsTOJERGriZsn0vAxWfDM7bZQHoGzAmaTcmnPAhFVWagxSkEPsbIeQqgAVQlAzY70RaTgH7TkREVlsaxw06qoeiHMzi1yDlcx0qHGYW8XCdleVCdu9dRERGebrvi4SVSGMMnNNZ6Az1FmiAeAWSgdg8NgfmJXYLNcV2TBTo8qkblVHGbyL4K2V5mYFKcGFJNuPCOI57aqZXpabZ4ozrtCQA6Bs1RFXP63ipveUGs+d/1295nf+8OdERORv/8N/4+v+1YuXfDkbmvgKaqC0G9qxjt+laaurUJf1bZzjB6vsG3cEVlAI2RVoQmeNim9+WePtvV/633z5hX/0KyIi8toHn/B1Z2vf82WnOkJV3YH8BixTU+wlNqlSIIcFHIsTxnLw48/68u/9v7/py1/68n8qIiIf/u3zcPZbk+9ZZs66Fwq7C9jYLPvmfoyhsoEQQgghhBBCCCGVwskGQgghhBBCCCGEVMrENIoQUia8MWSws277m2oQ2RYt/4f/1w9ERER3CBdJwVCuERp9WQeM6zatxHqmpfeJ6ypxaVjp6EZPpdozNZWszdbN8atrKsvu2jmVGqROBIJ7mNpCYZ9j6HS+s04v0WtyymbIg6Km6QupNStNY92n2Mk4+5meF6PPW80M5lC9WQtuPN2+TZvKIP3AmqGGMEVZr2vKQ83GZhuMKoMMzCBtvK/f6/i6e2JNI7to+qh9DkL7igJJeCgYx/Y4xHZkJXhhCqlSfZCMp+ahkxwMiQg5CPz3AVL0bOrSH/3fv+HrOhBnf+OxnxQRkV//6f/I130iek3bdN8ZGN8u3tH0EVMenZS7sJc5GiLb68KGxm7mDKIptSaPMoXUIxMzg0WNnc/82q/48un/5WUREfnw7/30xLby5IDNh+07AOP5wH9/0u2cHALC9W1f/g/gGykf2G/kJ56c3EBpmiPU7eV7V/Z/x2lpjB8zqGwghBBCCCGEEEJIpUxUNuBSfl7icuNW9T/39/9V+eX2+N/4++/5qmaq7SSdFXNasODrag2zZd+peZ2xbUU6J3Lh+HEREbl0UldUZ5p6PLLNf+OdW77uT94291kdwuonLOM6hUaSgEoBJ6pKTETyfOfsWJnfHCeDyUEwAoXQcGhmelMwa6wlRj2QzsA1Qw33emqUP41UFUB1mHts1sxMbFzXlc6Z9ryIiJxZUmO79hyYTjZMOYwgtiDmBnbry/dX1GAyvnZXRETurel2mWlhCtRcPwTDLYGtbhMbh1GwU/kQgSIqA0PNvOPiecrrj5C9UGqsuNP86kuPfdZXodljOGPKn/jK6xNv47bQFBEJ6nXbdPkKil9pBbVD2NBvaNA2sZvcvgM3cIE4xTSSkEcEt937+X+k2+sJfntszJ3/tW9rHSqAXUyigaM7jqojVBC5PaNx9RNWRb1qqWRb291sdbtvGPvkQTHBuDF958rO8+Dc9LUxppAupspUBkFJDMseFUquff7nrwCVDYQQQgghhBBCCKkUTjYQQgghhBBCCCGkUibqiEepGqi5WYkQ5GF5mayqRNYCim9J4Y41MVLvFxdVUvbCZ18UEZGT83piBg3UrRTm/AmVjIcgi0kSczy+fMrXbfbM8d97V+XbOezjGtjUjhik2Dm44OVe0gNy0tTIa1K4N87c5C5NoyTdgpD9kgx03PUTE6dhqsaLkZVlxwLnwZiu2VyFxlDHdK2p8X48MLLq9ozG5vKiSaM4e3ze1zVnVIo9a9OdIpCAxjD8+878tQESUbH3hPSq9QSO2/4FdX0fJKG+d5p9c30uKie1GSBSb+k1I7g+C827I+9q6gYhDxz4duQQz/nQjOUg1m+clKQxZkON12jW5kuh0eoQJNb2GxbOzWpdigaRphwtLurhtTVbp2mOWUfNufy1e5Ga7teQi5ADpJCOhOWSsVoITT+u4feljWNsM9/eGUcF+Tbk5Lrr9i3v3guMU/IwuN8Uvl2MybBu4jDDz6JNccJ4qiy2GC8FqGwghBBCCCGEEEJIpXCygRBCCCGEEEIIIZUyMY0iR5mllVPlYYmMDHdpyPSawLrC5yCFieKmlucumvPaOucx2N4QEZHVWG3059p6zdt3N0VE5LtvaErE9r0bvtw+fUlERELYg3zUNXLT2VD70c9gp43A/DMUPEpzlaim7pngOTMrXc/geeMIHE1dM3S8JwdAPtKYCWzKQ1BT2bUb/nkPHOth/KahG98g7YRzN/vGIbsGTtzpnJGXJeCA3d/WMd9NrNwT0jWaIP0Um5GRjVSm1rRxWANJeGYdwUVE8n5grwHZaaDPGeXm3DjF1A3znHGu743BSJ8zs/847r1AyKHCpR8W5NvwXbUx5XagEBGfEpH1NXZwhwux0tB0Vb+b0bymVGSb5ruaQ+w5CXUOsYnybt+ncVLrMkdxWxfAe+WBScIJuV/2KoF28u8cU5TSnW2WxQzERHxGd3zKVk06k9+1Aq8f189pxycxLXWCLvvkoCnbMWK/Tfb7O+rKdhUsA1OYouPHfDm5s2IbOiQ7Nh3i2KSygRBCCCGEEEIIIZUycdk9Elj9t7M1uJLv6grKBigHJbOrQaarkh92TfmNDV05ee3ehyIisrA85+uyVK9f3+iJiMjo2mv6EGtXffnLv2yUDXevq9rhyQWjkgib2ubL76/pc9rmF8BEbg5M7Bp2BQlXYwZ2tafX7fm6jb7Oxt0dUNFADo4czONyO25HsOLvTa1SVOiAoVzNKgZyUC6IrmqGmV09XVMTxfrIxvMA4h3mK7du3xIRkc2hxsFSW1dfZ5aN+Vy7rv1c2zDtx5nG0UKqK52hNY9t17Sf9QVdkY1iE9NZoPEW1ozSY2ukM9k3NjZ9ObHqqGSn5x4hh4cxqzteCQBmzU7REEJsCSiMghnzDUSVAuJWblDZEC0vmbYhdgpqiaxEgTHN0Oujz0DIw6JsFXC/q5L5ThWs5Pe3ShtAf/Kmfjd9zKK6yf4WDWdVAZxCnO7pOayxc1F1BO8LGt6Ro8a0GL/fMY0xsaQG6XL7zvhb1yCG9xtHLjZDeEccsW8olQ2EEEIIIYQQQgipFE42EEIIIYQQQgghpFImp1GgIaKYcj2C/e6trGuAe5QWJCK2nIOcEwzpBtYc7l5f5WF1a7I411dDOFBVy2zfGHL0c5WMjUBelqzdFBGRWx++Cf00DfzUz/2CrzveUvO4k7NGenr+mKZZtGr6HM2GkcPEYBLiTCMTkJ1euaXmW//4m1dFRORmv3qjE0KiZkP/YhVaYaxxltqYyDKUWusleWrqI9HgSjBNyJ5cH6gse9Qw5270NDaTjpavvvu6iIhs3rrt6+KGXr98+oyIiJw6cdbXHTtlzHaOP31Gz1vU2Dwxa1Ivlo9r6sRxkKf16qbP2QjMX+076Ppqx9e98v6qL7+eGIPLtTGSckIq5wD2qw/hG+ZIt7ZKzhSfcoESaZRd5z/5aXP8Wz/Q49aYzqVTYJ256D6fA5/d9iNaXtC648va/pvv3F+bhOyFA4jDPYGpxY3GjsPp2+/5cvT0EzvqgpZJFSyYRiL3axSH/wY2bSvH9K0wgpNLUkQIOQj2Y3CK4PVuLN9nWpOIxmYOpubp62/7crRovmMZ/A4OF0yaRbq2Ud6P0htNSffwsTmlw4c4NqlsIIQQQgghhBBCSKVwsoEQQgghhBBCCCGVMjGNog7O00FoZBwLLZV8dRMj2ehtqnQTZy/KFB31SM/I7W4XMWhDHp837T93atHXra5pesLGVldEREYg9bqzqXLpP/na10RE5JMv/pSvazTMcyzNtn3d+VO6j/EJm0ax2NZnCwPtU9s6A4fQ96GVYK93ur7uzQ91B4zUOuEHGcrQCKmGGMZnzQZamoEcMzTpPclQ5VlxTVMaAptaBFlRUgs1YKPAxEpjVqXaC3YXiJmmxsG9Dd1Fopab9rNUYyId6PgPIhOnpxe0H889Z9InLsDexY1Qj8/Nmg424V0UQdka4guo3KQzMP82cV/TJN5u6fG6TdtqBExxIg+GEKTSzkV6327SkBKRlaVPwG4VbheJguP9CKTckD7hCJ1UG13ud4O9F6Zu+D7jDhZ37o29lpBKeVAyY0w/KNtVBsa3j8mwfO0vfefKzsttSm+2rd/aPT1bSZwVd6M4Wo735BHhIOJ0zO5OE7sxNCm3hZiAVIZ0feOjl2hKxW7ud4jTH6qCygZCCCGEEEIIIYRUykRlw8yMKgGiyMy8rG6oQVR3aFdUUzThgNUUb/KhU0EhzPakmZk1/fHHVMXwxaeNWVQ20BnVDehlas3duls6ozQ7r2ZTL3z2RRERefEnv6DHrWJhOBhqP3BCN7d/gbo6rESNRmaF9NrVa77u66+YlaBXbuqK0uvr+mwbQ7P/cRhzhYZUTzDU1f/cmq6GMKiTvjleB1PTIAAjV6eMAFPJelMNUo8fM6uaZ+b1PieaZnY3GoHCZ1ENqkanjZFcq3HR152a1zYvPHleRESeeepJX3fxrFEYLbX1XZOAgU/kHinRuhHMAm9vm/rrm6qw2No2cX51VVdMb92468u9FbNSOwzU1IeQg8DttZ31xxi5Tby43MzOtYkGj37/bVAz4MpKPrAGU9BmtATGj2tg/Ogu75mYiuY0hnGl0x0vrMrgim1s3x3w/Xe/CYKZGW0T/m3cii1XVMmBU5UJHeAUTHnBNL3kPmi+bsd/AN9qZzwnAqunEFsuXgMwS8YftVPjxykv4B0RnTDf4gDMp3NQTJWt4hJykGBM5GmJUmA3sVsy5qdS0n4I365s2/yGxDh0aoh9A/Eenz5l2h6pWjfbUkV/7jYqCA+vkp7KBkIIIYQQQgghhFQKJxsIIYQQQgghhBBSKRPTKDY3VaaZ2n3sh5BrkNuUifqYVnIxEhSc0YgClaU8dcrIUX75Z5/3dRvbRlK2tqGmkEsNvcH1jpFyffqTz/m6z3/h5/Vcuyd4K1b5dyM30pOleTW7a0Kn66GRnN1bUan1q2+86cvf+NafiojIS994ydetxSb1Y/mn/6qv6yZ6z8yZz2WUg5LqGfY0BWAUO6NVjS3voZiB6WMy0AZsOcxVltVsqYviuQUjnb54TCVjx61BahLqmK7fVgn07PnT5rxnHvN1Z08d9+XTc6at9rzGnsu6GqTaj2SksuuOlZjeuf6hr3v/hr6X3vrBVRERefWG7n2cheY+QVNNYHuhyty69r0UR9M2LSZkf+SjfUgqx0hEXZvxubO+Lrlxc+I1TvqJ8uqy1Imy+6eb5QaRbg9yNJ3MhhrHrp8oMXUS7fT2nfJbOqksDSLJQXMApmxu/IeYitDrjTtdRETiM+a7mW2OSVmYkO4x7v3i5OcoQ88G8P23UnJM13DpTBm8FwrS9QNIOyFkEvtOnQBcqqFPa7qPtlxqEaYTZV0wZXVpC+gaWdYmfs9sqmMARuc5xKaL2RDSF53pZNbR1Imx7R9SqGwghBBCCCGEEEJIpUxUNgxhVim3MzcxGB4G1r0N/NwkgfmLup1tycHc7dSsrnL8tZ94QkREHluEVUe7jeWpRZ3VWWqo6cXxGbOl5bOXn/V18wvL2uehmSFqRHpPt3q7euemr3v/6ru+/J1Xvi8iIt/9vm7/9c677/nyVses7KSi/Vj6/JdFRKSXqloiSHSWuea2ycw5n0OqZ7C26ss+5iC22m5FI9Y4iEc6e5qn1thmoONz7rTG9nmrbDg1P+vrlhvmHVAXncVdrOksbhwaVdGJJb1mqalqn9B2qbelKz2ba8bE8Q4oLN6/qrPIr73+FyIi8t2Xv+nrbt7QVdHOprl+AOav9dmLpr+XPunraqdPimKeLahR2UAOCWPMIMuI3DaxdY2taddkPatA2o051hRDLbcaU7gzPoe9Pk80ttP1isyzCNkLaKDmViN3s1I6ZXXfb5G3i3hO766Y08aZOrrrS1ZHx8am/e1eaLNwvSmjgiI6Zn5Hh2AMn6ACyd0z55bR5AExLTZLDIkLagg0Sd6D6bBT+RSuLXmH5BlsjHCfJseoZsA2Xf9ReejMnJ1RpIhIuqL/B3Df2CA8vAoH/k+YEEIIIYQQQgghlcLJBkIIIYQQQgghhFTKxDSKoCCQNJKQIEdjRVNeaKt8ewAGkomVkUQjlbI8NqvzG5fPGGlIr6/SyiA10pKZphrTXbh0wZfDJ86JiEijrgY86VBl2Vsrt0RE5HvvvOPrXn31VRER+bMfaJrEu+9BmsSWTZPAPcRBihPZf4bmMZWwzJ0w/SjsOw5mkLlPuaBUm1RPY07Hv6yZcdcG9dhs3fyl2VbTxyBS2bXzRsTY/dQFNVT81DmTCjHX0vvM2UBIQdp2YlHlX5F9H8xAqlUO75Bbd43J41+A+eqtd66JiMi1OyrXvHPnli+/feUD84irKhlLBxpTWWjaj1pLvq5+2rwv2pee0L7NzfuyBOaZ0z73DScPgWlSbGfuBoat2TaYUlkTuuTqB74qbBvpc4ZmdNj+btIn/PUmzqJ5jZ1gXtMb09vGUBnTJAr3nCS3BtkoSj/zjOZz5ADBOPDmbruIjZKYRQNUf5ttNXAOmybVtmDQCO3ct7y7JLbQRA7TH9J7Vv6NBpLTJOn2N29yT7+1xXQQ/pYlh4xCHI121BVSHkq+gaUpDzDmS2Oz9FuqsZEn5v4FY2RIefQpjVPbBFxs3rqtdWWGsXtIFXlQfoXexQAAIABJREFUUNlACCGEEEIIIYSQSuFkAyGEEEIIIYQQQiplYhpFA2TXLivgmbPq6v7kGSO7vrCsOzKsd1Q+tmHL9URlI3Mjddgc9o00ZDBQ6cfcnJGCtRsqCQtAvTUzY+61tqay6z/+42/48ssvf1tERF5/Q3ebWLGSsmGiMrY0g0ZTJ0dRWUoU6T9NVDd9qR17XPtk68IMUkDgGrd7R54fXlkLObo889RFX+4OTRydm1PZ1slFMz5nIdsiA6lWv2PG7WxL4+Dy2UVfPr5gxvJcXcd0KOb6LAHpWgYuujbVqtvReL9y44Yvf+2r/05ERL753W/7uo1bxom7N9S+JRCbvW0jC08hXONId7uIZs0+4a2zT/u6pac+ZZ7thKY9DXs6r7pt07oCqkLJAYBpB046nQ9L5MxjJJ5OCol7e2Ob6aZJ+yvIPUe7/85Eiws7rsc9xJ2zfgrfdIE+7XW/c5HiHuNS5h5+BPYNJ0ePcEbTc32cDeFDcL9jGsZndPK4LyfX7feu4C4/ZdeLkrQqtzOEiEhuZddZH9IwbJxkEI9Y3gtuZwonLRc53LJs8miB487tfFJIA5pGSXxFS/qNS116EH4305L0BWynJDZdWpQIpEaVpTRA33f1HCWUffN3s+PNYYDKBkIIIYQQQgghhFTKRGXDz35aVwsX22bm5MkTusIyY2eFFmKd/RzFOqPbmzHKiATMcgZdmN8IbTnQWZl23dTVQq3rrOjqaOeGmeH56rf/zNf9k3/5u768cseYVqFwIbNzKlmgfQtzNbXKrblHUNNl4DooK+rW3CM+eU4bje3sFqxIZaIzz4GfEeOexKR6vvC0jsWuXZVs1TW2FmdsHMHMbToAI7eGNVYMIQ76qkgYbRlFQdIEA1Qbkyvrm76u09UZ29G2WVm5d/2Kr/vOX2icfufll8z1d9XkJu3b1aWa9j0TVWikuakPm/qqcqoiEZFa26ir6rNaNxiZOLx75Z72bV6P55l5zjiltIFUT46mVXblo7BaaGPSKQdERATKZUZXKa5alqy2+JUTNF6Eb3EQ2ZUiXKmEFdn8/Ws72zzAVc3CHuOlJxz+lRpy9MiH8L3bz2ojjM+szFARlUrT1DqB+/bB9whWZLO1jR1tTjOZ3RO2zdLVXkIOmMK4248iHJUL/ZLvTFnMjDOSLDu3Bop/+5u5oMpw3/J0p1rxfig1rXT924vR8yGBygZCCCGEEEIIIYRUCicbCCGEEEIIIYQQUikT0yj+5ucu+XLdyq7fv3nX1738NWPM+PxJ3Q8c9xYd2vSId9/8ka976ulnfDm0hnLr19XMcdtKxm7dVAPIt9/V4x+uGGl00j7t65bPaT/zyKRCpEOVoCR2SmUAsrmkq7LUVs3IbkJIeeh3NfUjbRq5aWtJzTHz1O51jnI50bJLo0hTGuyQ6lla1DhbSE15BGkQztRtfV3H+dqdFV+OpCMiIq2aysfasGd3tmVMVe/AdOSGNVr94Vsaj5sgIR32TOx2+hu+bntLZWxh06RgLSyrXDSzcVKvawpTv6YGkEOb+jECE9k8UZlcYK8L62puORqa4wMwb8039frUxmwzgrQSQirCpSyIiESnzDcjvaspPdHykqlb0zgJZzTNR+z4HSfDdOZxaYl8O5rX2Ek3NN0pH+2Ug6Zvv6d/mSDHLksBmXYNIYcRHL9h28QcmqJGT5nfkul7H8BFO9PtwlmNMzRmjE8bU+Lk1u0d1wSFVCn4XZjvTLNI39FUxFJKYm9Pxo4Fw7mStcf8gFM3CCmjZKzF586KSDFtKYPfvI5oAcyU4RsY2t+3pUaqJTGOuHeFyEdSM1w/MXYy0+ddpSOVmVYWDCCPfsovlQ2EEEIIIYQQQgipFE42EEIIIYQQQgghpFImplH0cj28um3kKm/cVAnKSz96TURErrVV4nFsVlMqFmpGyjUP8uzWnLrsXrtpZN1vv68S0+/9+fdN3TXdgWKrDxKS2Mimf/7HnvNVv/jsE77ctNMnTZBlX79jUjKugYx8s9Pz5bdeNWkeb37vZV+XgQSmfsbsypFF2mbadXu2wg4XkEKiaRRH1z2UHF5mGjpP2OsYWdftLZVlX339LVN35aqvC7qaArU4Y+Lw6ec0dhowvuvzptzfVpnaRs/cZ/O2SkQ3Ohq79Tmzh/kLn/5Lvu7ShfPa5+NG/j3K1M33Xsekc6yudrTvt6/78juvmud4/7U3fF0313Mzu6PNcFNTIoKTzu0fYg+NfZ3MLRvjDk7IPkg3O/A3W4Z0u8Dt052pHLTMNXucFNqlT2DKYmR3lsAdZaJFTS0Su6MSpnPcr7M1UyfIIwOmvQ537kYR2J2dCrGBTvVWzpz3elKGS59A2XXo0p5uaWowpjwEdRPHmM5R7FRJ+oLtUxCC/LrsfVHSd3uhOQzpW6Xy8rI0C+6wRh4GdovBaSlChW8p7hrjxjfERGR3fcGUxrLxPTY23SW4s42NmbFpU+60hv7edqkX5vb292sN0qKm7d50BKCygRBCCCGEEEIIIZUyUdnwpzfWfHlgZ4tu3tbZTzd5uwpmi1dg9vbsnDHR+etf/hlf99ynXvDlessoHo6d0dXPk5+4LCIiPwcGjyeXVQ2x2DJdXmjpjGzDrRSJyIwt10KdR+nYWaHVrs4+3VzXFaCvnzCrQj2YXbpxT1eA8sjUd1dVbZHaCd9WW42Cctzj3O1ZzJUgcgBEYPg27JlV/TtXdHy+9t0/FRGRzrW3fd1sW+Po8c+aVc9Lly/6ustnz/lye8bEUQIxcf5xc+7joFZIEzVSnZkxxjwXLj7p6xaXND5adRO70KRs9c0s7o1tXQ1+88oFfTYxZnp3PlTlwnbnVV/ud0wc5w3tR2TrwlhXcqL2jC/nDbMqVUtVhUVIVTgDRxGR3K6I4MpIct3EacHQDVZGwpmZHXVl4PFsbd38OWbF1a9K7mWfbviG4WqMW7kp3JPfO3KYCSYrAZIPTWxG82Aytwkmc85UskSJhGC8OwUF3g+VD27FdixlMWXjuOAbV6JiQOVDEEPsWjWFM5K+r3tT0UAOkrJxDvGa3LxlqkDRVzjV1mfTVADwDSyYLFtCUOI7lcS0b3EB+xyF90vB7NEex37i/x3td7WgvHoEzFmpbCCEEEIIIYQQQkilcLKBEEIIIYQQQgghlTIxjWJtVdMonCIkSFXOXA+MbGUYqjzr9LLKPB576jMiIvLEC5/zdXOLKlEJbarD/KxKTE4du2zb1n6EoBULJLd/wp7EKC1JjTRlmOg1oTVxbNfVmO7Ugj765198UUREGrNqqPU7f/RVX/7gxvum6UzloknNyMzDSNuMReU9oTPwCWhCR6pnlKgUbCM1UsjRQCWRw5FJbRqFKuVaPHvclx97/lkREblw+qweh3SlmpVZ5jB+23PmnscXNDUizFX+Fc+YWEC5Zj3S6zM7tznEeLby7tmmtvnsBZgDDUw/k22t+97LGrudvjHkGkHs5ZE5HmVg0NPV91YYmn7mektCKiOYg5SdbRuTJeZupfva7xEv2x73vdlL+kQJKP10aYPR8pKvS+E3w1GWfJJHkxDSgLKh/Sbg9whM3Upxx3cRT15ODbF5IKlHJX0KFzQdBI3zUmeWxxglh4WSVAP8Rgb2t2TQ1BjG9AZvZLyXMY2xWWaUul+mpIgUjGtdGY67tKd8lJRecxSgsoEQQgghhBBCCCGVMlHZcGZBV2hGdtZoFOjqf2PGlD8An4v6gq6e/swXPysiIstzuoQ4AsVBZg1nOmByU4/N/MdcuQeIxLmZ7QkjnSeJwpLteQozRSVmjVBcnDdqi8tPXvJ1r715xpevXzfKhgTajOyqTp7jbBwU3TYt5Y9ByL7IM53hbKdmzDcWNc5OnXlKRESCZTWre8aar4qInDn/mIiI1Osa4xluo5WZNvsFVZEZ83FdjRVbTYjDuObPdAwSjYCRNYMcwBRnZGOyDrE509T2w3MnRURk+EVVEHVFZ7Nfe9Vsv9vpwUuoZ/qcxVoXij5H3jYz4+nR302IHEIyMEn2RnKwShHalRk0kUNTqqkrK5PMova6UunaRLVF2cpJyWrMWDXDI2BqRR4tMtga1gPmbG6lNEXD1nbJ9pBlq7DTeAhxUGaAJyLjFVAfBU0nj9hKKjlilMRH2dauOXwf0SzSqxyOSGwWjJdxK9yW+f1bMIh0rupHOAapbCCEEEIIIYQQQkilcLKBEEIIIYQQQgghlTIxjeKJ47DXcGYkHeuwd313waRRPL2kBlFPfvYFXz537nERERmO1JwtAsO4fEdBJLNykRyN5zBlws6PoLwGG/CpEmVbE8N+xu4+IiKN2Nxrvt30dU89/rgvv/veeyIicm1V91vOY2sQGai8G80gQ9u/PKOElFQPeORIYlMdlkGK/cJnnxMRkbkZSK04rfG8cMyaQdYx5UHjLBEj14LQk8TGSRPGeYpj3pfxuF7fs/1EIVjqwiPVeB8GesaMTXl48qSaVz5zUWPz+u0N0/bb7/q6kdsPfKRyu1FTY7uRm9defY5zraR6MD1CZZ7RjuMoAc06HW0gnGJC582z9iAXRdCAqsQYz6cIFtIPd5EmsR9pKo2VyQET2m9CnkKqYKtZ+FNEJF25t/PiRzE1yL53QvxxAb+Z88T+pk0SIaRypnyPXKqBM0sUKX5rH5W0Pfec2TY8G6QzH1X4a5sQQgghhBBCCCGVwskGQgghhBBCCCGEVMrENIrjc+oKPxqaUztdcMH/pNlt4jykW1x+4oQv1+1cRljT29RAHVmzSpkYFDOBlWDHgUphCptN2HIY6jxJWHDRt2kYINbObZdHufY9h2siK3Gdaal87NOfetaXBzYn4/e/+Yqvu7PRt32Ddgr7pts9YSkHJQfAPOwXfmLBpErMNVVeVj9j4nD5mDppt0Ee2bIytbipsdkKtZyGJn7CWNOEZmzM1SC3IoS0qMgexyhIIo1jF+ewQYWk9i8RnBdlkJoRm/LZRX22S5dP+/K1e8YJv7Ny19f1+lam3lXpWRrD+6Rlehi19b1FyIFg5Y8oPY4WTUpQ1gNn/EKqwn06Tu9XLlq69zdG75TdKFwVOGlj2mBYN++O0h0ACHnIeHf7krSAfHDEtyq6X0l5ye4y+ag8TYLpE+RBk6f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      "text/plain": [
       "<Figure size 1440x1440 with 55 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_outlier_image(od_preds, \n",
    "                           all_X_mask, \n",
    "                           X_recon=all_X_recon, \n",
    "                           max_instances=all_X_mask.shape[0], \n",
    "                           n_channels=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict outliers on a subset of features\n",
    "\n",
    "The sensitivity of the outlier detector can not only be controlled via the `threshold`, but also by selecting the percentage of the features used for the instance level outlier score computation. For instance, we might want to flag outliers if 40% of the features (pixels for images) have an average outlier score above the threshold. This is possible via the `outlier_perc` argument in the `predict` function. It specifies the percentage of the features that are used for outlier detection, sorted in descending outlier score order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_list = [20, 40, 60, 80, 100]\n",
    "\n",
    "all_perc_scores = []\n",
    "for perc in perc_list:\n",
    "    od_preds_perc = od.predict(all_X_mask, outlier_perc=perc)\n",
    "    iscore = od_preds_perc['data']['instance_score']\n",
    "    all_perc_scores.append(iscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize outlier scores vs. mask sizes and percentage of features used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K9iq7ujMEq5ryC0/HpZTyLC2BKa8nIvWxqvC6Yt1v+xp40L4ZrZSqoTSBKaWUqpK0ClEppVSVVGmdwrpb06ZNTdu2bT0dhlJKVSlbtmw5aYxp5uk4zkW1SWBt27YlKqqk1tJKKaWKIyLn2uOKx2kVolJKqSpJE5hSSqkqya0JzO5uaq+IxEoxHTOKyCMisltEfhWRVSLi3PdggYhst/+WuDNOpZRSVY/b7oHZfci9i9VlSTywWUSWGGN2O022DYgwxmSLyBTgVawudQBOG2OK7Z/PVXl5ecTHx5OTk3M+X+N1/Pz8CA0NpXbt2mVPrJRS1ZQ7G3H0A2KNMQcARGQ+1qtMfktgRfq52oDV92CFiY+Px9/fn7Zt2yLiaofc3s0YQ3JyMvHx8bRr187T4SillMe4swoxhLPfXxNPye+BAavX8OVOn/1EJEpENojINcXNICJ32dNEnThx4g/jc3JyaNKkSbVJXgAiQpMmTapdqVIppcrLK5rRi8jNQARWT8aFwowxCSLSHlgtIjuMMWe9hNEY8wHwAUBERESxXYpUp+RVqDquk1JKlZc7S2AJnP1yvFDOfhkkACIyAus1DGOcXseCMSbB/vcA1uute7sxVqWUqnKMMXyz8xjzNx3xdCge4c4EthnoJNYLGutgvTfnrNaEItIb6zXYY4wxx52GB4n1UklEpCkwCKd7Z1VJXFwcl112Gd26daN79+68/fbbAKSkpBAZGUmnTp2IjIwkNTXVw5EqpaqSfUmZ3Dx9I/d8soUFUXHUxH5t3ZbA7DcJ34/1grw9wAJjzC4ReUFExtiT/QtoCCws0lw+HIgSkV+ANcDUIq0XqwxfX19ef/11du/ezYYNG3j33XfZvXs3U6dOZfjw4cTExDB8+HCmTp3q6VCVUlVAenYezy3ZxZVv/8jOhAxeGNudBXcPqJG3Ftx6D8wYswzr7bnOw/7u9P8RJcz3M9DTnbFVlpYtW9KypfWSVH9/f8LDw0lISGDx4sWsXbsWgIkTJzJ06FBeeeUVD0aqlPJmBQ7Dp5vjeO27vaRl53Jj/zY8EtmFxg3qeDo0j/GKRhyV4fmvdrH7aMW+PqpbqwCeHd3d5ekPHTrEtm3b6N+/P0lJSb8lthYtWpCUlFShsSmlqo/Nh1J4bskudh3NoF+7xjw3ujvdWgV4OiyPqzEJzNOysrK47rrreOuttwgIOHvHE5EaWfxXSpUuMf00Ly+LZskvR2nVyI93JvRm1AUt9XxhqzEJrDwlpYqWl5fHddddx0033cS1114LQPPmzUlMTKRly5YkJiYSHBzssfiUUt4lJ6+AaT8e4N01+ykwhgeGd2LKkA7Uq+Pj6dC8inbm62bGGCZPnkx4eDiPPPLIb8PHjBnDzJkzAZg5cyZjx471VIhKKS9hjOHbXceIfPN7XvtuH0O7NGPVI0N4JLKzJq9i1JgSmKesW7eO2bNn07NnT3r1srp2fOmll3jqqacYP34806dPJywsjAULFng4UqWUJ8UkZfL8V7v5KfYknZs3ZO4d/RnYsamnw/JqmsDcbPDgwSU+n7Fq1apKjkYp5W3ST+fx1sp9zFp/mAZ1fHhudDduvjgMXx+tICuLJjCllPKAAodhQVQc//p2L6nZuUzo14bHLq/ZzeLLSxOYUkpVsqhDKTz31S52JmTQt20Qz47uR4+QRp4Oq8rRBKaUUpXkWHoOLy/fw+LtR2kR4Me/J/RmtDaLP2eawJRSys1y8gqY/tNB3l0TS77D8JdhHZkytAP16+gp+Hzo1lNKKTcxxrBidxL/+HoPR1KyuaJ7c56+uhutG9f3dGjVgiYwpZRyg9jjVrP4H2NO0im4IZ9M7s/gTtosviJpAqskBQUFREREEBISwtKlSzl48CA33HADycnJ9OnTh9mzZ1OnjrY+UqqqSz+dx9srY5i1/hD16vjwrN0svrY2i69wukUrydtvv014ePhvn5988kkefvhhYmNjCQoKYvr06R6MTil1vhwOw6ebjzDstbV89PNB/hzRmrWPDWXSoHaavNxEt2oliI+P5+uvv+aOO+4ArHrx1atXM27cOMB6ncqXX37pyRCVUufhWHoOf/rvOp78bAftmjbgq/sH8/K1PWnSsK6nQ6vWak4V4vKn4NiOiv3OFj3hyrJfRPnQQw/x6quvkpmZCUBycjKBgYH4+lqbPzQ0lISEhIqNTSlVKY6l53DDB+s5mZXLW9f3YmyvVtosvpJoCczNli5dSnBwMH369PF0KEqpCuacvGbe3o9reodo8qpENacE5kJJyR3WrVvHkiVLWLZsGTk5OWRkZPDggw+SlpZGfn4+vr6+xMfHExIS4pH4lFLnpmjy6hMW5OmQahwtgbnZyy+/THx8PIcOHWL+/PkMGzaMOXPmcNlll7Fo0SJAX6eiVFWTmH5ak5cX0ATmIa+88gpvvPEGHTt2JDk5mcmTJ3s6JKWUCxLTTzPhgw2avLxAzalC9AJDhw5l6NChALRv355NmzZ5NiClVLk4J69Zk/txURtNXp6kJTCllHKBJi/voyUwpZQqg3XPawPJmry8ipbAlFKqFIXJK0WTl9fRBKaUUiVwTl4zNXl5HU1gSilVjKNpmry8nd4DU0qpIo6mnWbCh79XG/bW5OWVtARWCdLS0hg3bhxdu3YlPDyc9evXk5KSQmRkJJ06dSIyMpLU1FRPh6mUQpNXVaIJrBI8+OCDjBw5kujoaH755RfCw8OZOnUqw4cPJyYmhuHDhzN1qme6ulJK/U6TV9WiCczN0tPT+eGHH37raaNOnToEBgayePFiJk6cCOjrVJTyBs73vDR5VQ015h7YK5teITolukK/s2vjrjzZ78lSpzl48CDNmjVj0qRJ/PLLL/Tp04e3336bpKQkWrZsCUCLFi1ISkqq0NiUUq4rTF6ppzR5VSVaAnOz/Px8tm7dypQpU9i2bRsNGjT4Q3WhiOgrGJTyEOfkNfuO/pq8qhC3lsBEZCTwNuADTDPGTC0y/hHgDiAfOAHcbow5bI+bCDxtT/oPY8zM84mlrJKSu4SGhhIaGkr//v0BGDduHFOnTqV58+YkJibSsmVLEhMTCQ4O9kh8StVkRZNXr9aBng5JlYPbSmAi4gO8C1wJdAMmiEi3IpNtAyKMMRcAi4BX7XkbA88C/YF+wLMiUiUvi1q0aEHr1q3Zu3cvAKtWraJbt26MGTOGmTOtnKyvU1Gq8iVo8qry3FkC6wfEGmMOAIjIfGAssLtwAmPMGqfpNwA32/+/AlhhjEmx510BjATmuTFet3nnnXe46aabyM3NpX379nz00Uc4HA7Gjx/P9OnTCQsLY8GCBZ4OU6kaIyHN6pg3NVuTV1XmzgQWAsQ5fY7HKlGVZDKwvJR5//DKYhG5C7gLoE2bNucTq1v16tWLqKioPwxftWqVB6JRqmY7K3lN1uRVlXlFIw4RuRmIAP5VnvmMMR8YYyKMMRHNmjVzT3BKqWpDk1f14s4ElgC0dvocag87i4iMAP4PGGOMOVOeeZVSylXWPa/1mryqEXcmsM1AJxFpJyJ1gBuAJc4TiEhv4H2s5HXcadS3wOUiEmQ33rjcHqaUUuVWmLzSsvP4RJNXteG2e2DGmHwRuR8r8fgAM4wxu0TkBSDKGLMEq8qwIbDQfg7qiDFmjDEmRURexEqCAC8UNuhQSqnyKJq8LtTkVW249TkwY8wyYFmRYX93+v+IUuadAcxwX3RKqepOk1f1VmO6klJK1SzxqdlM+HCDJq9qzCtaIVZ3b775Jt27d6dHjx5MmDCBnJwcDh48SP/+/enYsSPXX389ubm5ng5TqWpDk1fNoAnMzRISEvj3v/9NVFQUO3fupKCggPnz5/Pkk0/y8MMPExsbS1BQENOnT/d0qEpVC87Ja84dmryqM01glSA/P5/Tp0+Tn59PdnY2LVu2ZPXq1YwbNw7Q16koVVES7Pd5FSavC0I1eVVnNeYe2LGXXuLMnop9nUrd8K60+NvfSp0mJCSExx57jDZt2lCvXj0uv/xy+vTpQ2BgIL6+1uYPDQ0lIUEfc1PqfCRnneHmaRs1edUgWgJzs9TUVBYvXszBgwc5evQop06d4ptvvvF0WEpVK6fO5DPp480kpp/m40l9NXnVEDWmBFZWScldVq5cSbt27Sjs6uraa69l3bp1pKWlkZ+fj6+vL/Hx8YSE/KGrR6WUC3LzHdzzyRZ2Hc3g/Zv70CessadDUpVES2Bu1qZNGzZs2EB2djbGmN9ep3LZZZexaNEiQF+notS5cjgMjy/6hR9jTvLytT0Z0a25p0NSlUgTmJv179+fcePGcdFFF9GzZ08cDgd33XUXr7zyCm+88QYdO3YkOTmZyZMnezpUpaoUYwz/+HoPi7cf5YmRXRgf0brsmVS1IsYYT8dQISIiIkzRV5bs2bOH8PBwD0XkXtV53ZRyxf++38/U5dFMGtSWv4/qht0dnSonEdlijInwdBznQktgSqkqZ2FUHFOXRzPmwlY8c7Umr5pKE5hSqkpZHZ3EU5/vYHDHprz25wupVUuTV02lCUwpVWVsOZzKvXO20r1VAP+7pQ91fPUUVpPpr6+UqhJikjK5/ePNtGxUjxm39aVh3RrzFJAqgSYwpZTXO5p2mltnbKKOby1m3d6Ppg3rejok5QU0gSmlvFpadi4TZ2wiKyefmZP60bpxfU+HpLyEJrBKcPvttxMcHEyPHj1+G5aSkkJkZCSdOnUiMjKS1NRUwHq25YEHHqBjx45ccMEFbN261VNhK+Vxp3MLuP3jzRxOyebDiRF0axXg6ZCUF9EEVgluu+22P/R/OHXqVIYPH05MTAzDhw9n6tSpACxfvpyYmBhiYmL44IMPmDJliidCVsrj8goc3Dd3K9vi0vj3Db24uH0TT4ekvIwmsEpw6aWX0rjx2f2zLV68mIkTJwJnv05l8eLF3HrrrYgIF198MWlpaSQmJlZ6zEp5kjGGv36+g9XRx3lxbA9G9mjp6ZCUF6oxzXh+XLCPk3FZFfqdTVs35JLxnc9p3qSkJFq2tA7KFi1akJSUBFgvwGzd+vcucQpftVI4rVI1wavf7mXRlngeGtGJmy8O83Q4yktpCcwLiIj2JKCUbfpPB3lv7X5u6t+GB4d38nQ4yovVmBLYuZaU3KV58+YkJibSsmVLEhMTCQ4OBqwXYMbFxf02nb5qRdUki7cn8OLS3Yzs3oIXxvbQCztVKi2BeciYMWOYOXMmcPbrVMaMGcOsWbMwxrBhwwYaNWqk1YeqRvhh3wkeW/gL/ds15q0beuGjXUSpMtSYEpgnTZgwgbVr13Ly5ElCQ0N5/vnneeqppxg/fjzTp08nLCyMBQsWAHDVVVexbNkyOnbsSP369fnoo488HL1S7vdLXBr3fLKFjsH+fDgxAr/aPp4OSVUBZSYwscrwNwHtjTEviEgboIUxZpPbo6sm5s2bV+zwVatW/WGYiPDuu++6OySlvMaBE1lM+ngzjRvUYeakvgT41fZ0SKqKcKUK8b/AAGCC/TkT0DOsUuq8Hc/I4dYZmxBg9uT+BAf4eTokVYW4UoXY3xhzkYhsAzDGpIpIHTfHpZSq5jJy8rh1xiZST+Uy766Lade0gadDUlWMKyWwPBHxAQyAiDQDHG6NqgJVlzdOO6uO66Rqlpy8Au6cGcX+E1n875Y+XBAa6OmQVBXkSgL7N/AFECwi/wR+Al5ya1QVxM/Pj+Tk5Gp1wjfGkJycjJ+fVrWoqqnAYXho/nY2Hkzh9fG9uKRTM0+HpKqoMqsQjTFzRGQLMBwQ4BpjzB63R1YBQkNDiY+P58SJE54OpUL5+fkRGhrq6TCUKjdjDM8s3sk3u47x7OhujLmwladDUlVYqQnMrjrcZYzpCkRXTkgVp3bt2rRr187TYSilbG+vimHuxiPcO7QDkwbpsanOT6lViMaYAmCv3XS+3ERkpIjsFZFYEXmqmPGXishWEckXkXFFxhWIyHb7b8m5LF8p5T0+2XCYt1bGMD4ilMev6OLpcFQ14EorxCBgl4hsAk4VDjTGjCltJrv09i4QCcQDm0VkiTFmt9NkR4DbgMeK+YrTxpheLsSnlPJyy3ck8szinQzvGsxLf+qpXUSpCuFKAnvmHL+7HxBrjDkAICLzgbHAb3Zx48wAACAASURBVAnMGHPIHldlWjUqpcpn/f5kHpy/nYvaBPGfGy/C10d7sFMVo8w9yRjzPdb9L3/7b489rCwhQJzT53h7mKv8RCRKRDaIyDXFTSAid9nTRFW3hhpKVQe7j2Zw16wowprUZ/rECOrV0S6iKlxuNqQe8nQUHlFmAhOR8cAm4M/AeGBj0ftVbhJmjIkAbgTeEpEORScwxnxgjIkwxkQ0a6ZNcZXyJnEp2Uz8aBP+fr7MmtyPwPra/0GFi1kB/70YPr0ZHDWvIsuVKsT/A/oaY47Dbw8yrwQWlTFfAtDa6XOoPcwlxpgE+98DIrIW6A3sd3V+pZTnnMw6wy3TN5JX4GDenQNo2aiep0OqXjIS4ZsnYfdiaNoZRk6FWjWvataVBFarMHnZknHtAejNQCcRaYeVuG7AKk2VSUSCgGxjzBkRaQoMAl51ZV6llGedzDrDbR9t4lhGDnPuuJiOwf6eDqn6cBTA5mmw6kVw5MGwp2HgA+Bb19OReYQrCewbEfkWKOxS/XpgeVkzGWPyReR+4FvAB5hhjNklIi8AUcaYJSLSF6uXjyBgtIg8b4zpDoQD79uNO2oBU4u0XlRKeaHoYxlM/jiK5FNneO/mPvQJC/J0SNXH0e2w9CE4ug06DIOrX4fG7T0dlUeJK90sici1wGD744/GmC/cGtU5iIiIMFFRUZ4OQ6kaa3V0En+Zu40GdX2ZNjFC+zesKGcyYfU/YdP7UL8pjHwZelwHFfQogohssdsbVDmuvA+sHbDMGPO5/bmeiLQtbAKvlKrZjDFM/+kgLy3bQ3jLAKZNjNB7XhXBGNizBJY/BZmJ0HcyDHsG6umFQSFXqhAXAgOdPhfYw/q6JSKlVJWRV+Dg74t3Mm9THCO7t+CN6y+kfh190ft5Sz0Myx6HmG+heU+4fjaEVslCklu5sqf5GmNyCz8YY3L1fWBKqbTsXKZ8spX1B5K5d2gHHru8C7VqaQ8b56UgD9a/C9+/Aghc/k/ofw/46EVBcVzZKidEZIwxZgmAiIwFTro3LKWUNztwIovJM6NISD3N63++kOv66NsRztuRjVYjjeO7ocvVcOUrENi67PlqMFcS2D3AHBH5D9brVOKAW90alVLKa/0ce5Ipc7biU0uYc2d/+rZt7OmQqrbsFFj5HGydCQGhcMNc6Hq1p6OqElx5H9h+4GIRaWh/znJ7VEoprzR34xH+vngn7Zo2YMZtfWnduL6nQ6q6jIFfF8C3f4PTqTDgfhj6V6jb0NORVRklJjARGQ38aow5bA96BLhORA4DDxpjDlZGgEopzytwGP7x9W4+WneIIZ2b8c6NvQnwq+3psKquk7Hw9SNw8HsIiYBbv4QWPT0dVZVTWgnsn8DFACIyCrgZmIDVpdP/gCvcHp1SyuMyc/J4YN421uw9waRBbfm/q8K1R/lzlZcD696CH18H33rWw8h9JkEt7eT4XJSWwIwxJtv+/7XAdGPMFmCLiNzr/tCUUp4Wl5LNHTOjiD2RxT//1IOb+od5OqSq68D3VqkrORZ6jIMrXgL/5p6OqkorLYGJfd8rGxgO/NdpnJ9bo1JKeVzUoRTunr2FvAIHMyf1Y3Cnpp4OqWrKOgHf/R/8+ikEtYObP4eOwz0dVbVQWgJ7C9gOZGC9AywKQER6A4mVEJtSykO+2BbPk4t20CrQj+m39aVDM21YUG4OB2ybBSuehdxTcOnjcMmjUFt7KakoJSYwY8wMuxPfYOAXp1HHgEnuDkwpVfkcDsPrK/by7pr9XNy+Me/d1IegBtpvQbkl7YalD0PcBggbDKPegGZdPB1VtVNqM3r7nVwJRYZp6Uupaig7N59HF/zC8p3HuKFva14Y24M6vtpYo1xyT1m9aKx/F+oGwDXvwYUTKqzjXXU27Z9EKcWx9BzumLWZXUczePrqcCYPbofoSbd89n0HXz8K6Ueg980Q+SLU14e83UkTmFI13I74dO6YtZmsnHym3RrB8HBtGVcup9Pgm6fgl3nQtAvctgzaDvJ0VDVCqQlMRHyAXcaYrpUUj1KqEi3fkcjDC7bTpEFdFk0ZSHjLAE+HVLXEroLF90NWEgx5Ei55DHz1nmFlKeseWIGI7BWRNsaYI5UVlFLKvYwxvLsmlte+20fvNoF8cEsEzfxr5mvpz8mZLFjxDETNsEpdN8yBkIs8HVWN40oVYhCwS0Q2AacKBxpjxrgtKqWU25zJL+Cpz3bwxbYExvZqxSvXXYBfbe0JwmWHf4Yvp1jv7Br4F7jsaaitj8Z6gisJ7Bm3R6GUqhQns85w9+wtbDmcyqORnbl/WEdtrOGqvBxY/aLVwjAoDCYtg7CBZc+n3MaV3ui/F5EwoJMxZqWI1Af0ck2pKmbvsUwmz9zMyawzvHvjRVx9QUtPh1R1JGyBL6bAyb0QMRkiX9Be471AmQlMRO4E7gIaAx2AEKzOfLUvFKWqiDXRx/nLvG3Ur+PDgrsHcEFooKdDqhryc+GHf1md7/q30G6gvIwrVYj3Af2AjQDGmBgRCXZrVEqpCmGMYca6Q/zz692Etwxg2sQIWjbSroxckrQLvrgHjv0KF94II1+Gepr4vYkrCeyMMSa3sJ5cRHwB49aolFLnLa/Awd8X72LepiNc0b05b17fi/p19NHPMjkK4Od/w5qXwK8RXD8Hwkd5OipVDFf25u9F5G9APRGJBO4FvnJvWEqp85FyKpd752xhw4EU7h3agccu70KtWtpYo0wnY60WhvGbIHwMjHoTGmgv/N7KlQT2FDAZ2AHcDSwDprkzKKXUuYs+lsEdM6M4nnmGN8ZfyLUXhXo6JO/ncMDmD62e433rwnXTocd12oehl3OlFaID+ND+U0p5sW92JvLIgl9oWNeXBXcPoFdrvWdTprQjsPg+OPgDdIyEMe9AgLbQrApKTGAissAYM15EdlDMPS9jzAVujUwp5TKHw/Dv1TG8tTKGC1sH8sEtfWgeoA/XlsoY2PYJfPNXwFiJq/ctWuqqQkorgT1o/6t3L5XyYqfO5PPYQus1KNdeFMJLf+qpPWuUJfMYLHkAYr6FtpfAWPvhZFWllPZCy0T738OVF45SqjziUrK5c1YU+5Iy9TUortqxyHrtSX4OjHwF+t0FtfS9Z1VRaVWImVhVh8LZVYgCGGOMdlutlAdtOJDMvXO2klfg4KNJ/RjSuZmnQ/Jup5Lh60dg95cQ2td62WTTTp6OSp2H0kpg/pUZiFLKdZ9sOMxzS3bRpkl9pt0aQftm2q1RqaKXwVcPWO/uGv4sDHwAfPSZuKrOla6kZhtjbilrmFLK/XLzHTz/1S7mbDzCZV2a8faE3gT41fZ0WN4rJ91qpLF9DjTvCbd8CS16eDoqVUFcqfjt7vzB7omjjytfLiIj7feJxYrIU8WMv1REtopIvoiMKzJuoojE2H8TXVmeUtVZctYZbp6+kTkbj3DPkA5Mm9hXk1dp9q+B/w6EX+bDpY/Dnas1eVUzpd0D+ytQ2ANHRuFgIBf4oKwvtt/m/C4QCcQDm0VkiTFmt9NkR4DbgMeKzNsYeBaIwLr/tsWeN9XF9VKqWtl9NIM7Z0VxMusMb13fi2t6h3g6JO+VewpW/B02T4OmnWHyCgh16Zq7StqVvIvk08lcGnqpp0OpdKXdA3sZeFlEXjbG/PUcvrsfEGuMOQAgIvOBscBvCcwYc8ge5ygy7xXACmNMij1+BTASmHcOcShVpS3fYT2cHFDPejj5Qn04uWSHf4Yv74XUQzDgfhj2NNSufp0X5xXk8d3h75gbPZdfT/xKh0YduCTkkhrXAtWVu5jLReQPqd0Y80MZ84UAcU6f44H+LsZV3Lx/uOQUkbuwXvVCmzZtXPxqpaoGh8Pw9qoY3l4VQ+82gbx/cx+C9eHk4p1OtUpdW2dBYBjcthTaDvZ0VBXuePZxFu5byMK9C0nOSaaNfxue7PskYzqOqXHJC1xLYI87/d8Pq2S1BRjmlojKwRjzAXZ1ZkREhPaQr6qNU2fyeWTBdr7dlcS4PqH845oe+nBycYyBnZ/BN09BdorVunDoU1CngacjqzDGGLaf2M7cPXNZeXglBaaAwSGDuTH8Rga2GkgtqbnPsLnSF+Jo588i0hp4y4XvTgBaO30OtYe5IgEYWmTetS7Oq1SV5vxw8jOjunH7oLY18uq6TKmHrAeSY1dCq4usl022rD493OXk57D84HLmRc9jT8oe/Gv7MyF8Ajd0uYE2AVrjBK6VwIqKB8JdmG4z0ElE2mElpBuAG11cxrfASyISZH++HDiX+3BKVSk/7z/JfXO2UuAwfDypH5fqw8l/VJAHG/4La16GWj5w5avQ9w7r/9XA0ayjzN87n89jPif9TDodAzvyzMXPMKr9KOrXru/p8LyKK8+BvcPvPXHUAnoBW8uazxiTLyL3YyUjH2CGMWaXiLwARBljlohIX+ALIAgYLSLPG2O6G2NSRORFrCQI8EJhgw6lqiNjjPVw8le7ade0AR/eGkG7ptWnGqzCxG+Brx6EpB3QdZSVvBpV/RaZxhg2HtvI3D1z+T7+ewCGtR7GjeE3EtE8QkvgJRBjSr91VOQZrHzgkDFmnVujOgcREREmKirK02EoVW65+Q6eXWK9OXlY12DevqEX/vp819nOZMKqF2HTB+DfAq76F4SPLns+L5edl82S/UuYFz2PA+kHCKobxHWdr2N85/G0bFg5r3QRkS3GmIhKWVgFc6UK8VOgo/3/WGNMjhvjUapGOZl1hns/2cqmQ9abkx+9vAs++ubks+1ZCsseh8xE6HcnDHsG/Kp2V6yH0g8xf+98FscuJisvi25NuvGPQf9gZLuR1PWp6+nwqozSHmT2BV4CbgcOYz3E3FpEPgL+zxiTVzkhKlU97Tqazl2ztnAy6wxv39CLsb2qflVYhUpPgOVPQPRSaN4Drp8NoVWyoACAwzj4KeEn5kbPZV3COnxr+XJ52OXcGH4jFzS9QKsJz0FpJbB/Af5AO2NMJoCIBACv2X8PljKvUqoUX/+ayKMLtxNUvw6L7hlIz9BGng7JezgKrF40Vr0IjnwY8TwMuA98qma1avqZdL6M/ZL50fOJz4qnWb1m3NvrXv7c+c80rdfU0+FVaaUlsFFAZ+N0k8wYkyEiU4BoNIEpVW4Oh+HNlft4Z3UsfcKCeO/miwj214eTf3Nsh9VII2ELdBgGV78Bjdt5Oqpzsi91H/Oi5/H1ga85nX+a3sG9efCiBxneZji1q2gy9jalJTBjimnhYYwpEBF9aFipcso6k8/Dn25nxe4kxkeE8uI1PajrWz2afp+33Gz4fir8/B+oFwTXToOe46CKVavlO/JZE7eGedHz2HxsM3V96nJVu6uY0HUC4U1cefpIlUdpCWy3iNxqjJnlPFBEbsYqgSmlXHQkOZs7Zm1m/4lTPDu6G7cN1IeTfxO7EpY+AmmHofctEPkC1G/s6ajKJf1MOgv3LWR+9HySspNo1aAVD/d5mGs7Xkugn/Zd6S6lJbD7gM9F5HasrqPA6h2+HvAndwemVHWxLvYk983dijEw6/Z+DOqo9z0AyDpuvatr5yJo0gluWwZtB3k6qnI5kX2C2btn8+neT8nOz6Z/y/78rf/fGBI6BJ9q8mC1NyutN/oEoL+IDOP3d4ItM8asqpTIlKriTucW8Oq30Xz88yE6NmvItIkRhDXRh5NxOGDbbKvz3bxsGPpXGPww+Fad5uNxmXF8tPMjFscuJt/kc0XbK5jcYzJdGnfxdGg1iit9Ia4GVldCLEpVG5sOpvD4ol84nJzNLReH8dSVXWlQV19hz4l9sPQhOLwOwgbBqLegWWdPR+WyvSl7mb5zOt8e+hYf8WFsx7FM6j5J+yb0ED2ilKpA2bn5vPrNXmauP0RoUD3m3tmfgR20ypD8M/DjG/DTG1C7Pox5B3rdDLWqRk/q249vZ9qOaXwf/z31fOtxS/gt3Nr9VoLrB3s6tBpNE5hSFWTjgWSe+OxXDidnM3FAGE+M1FIXAId+gq8eguQY6PlnuOJlaOj9nRQbY/j56M98uONDtiRtIbBuIPf2upcbu95Io7r63J430KNLqfNUWOr6+OdDtGlcn/l3XczF7Zt4OizPy06BFc/Atk+sl0ze/Bl0HOHpqMpU4Chg5ZGVTN8xnT0pewiuH8wTfZ/guk7XaW/wXkYTmFLnYf3+ZJ787FeOpGRz28C2PDGyC/Xr1PDDqiAfdiyE75623pQ86CEY8iTU8e6Tf15BHl8d+IqPdn7EoYxDhAWE8fzA5xnVfhR1fOp4OrwSFWRkkJ+URN1OnTwdSqWr4UeaUufm1Jl8XvkmmlnrDxPWpD6f3nUx/Wt6qetMJmydDRvfg7QjENIHbv0SWvT0dGSlys7L5rOYz/h418cczz5OeONwXhvyGiPajPDapvD5J06QuWo1mStXcmrjRvy6dKHdooWeDqvSaQJTqpx+3n+SJxb9SkLaaSYNasvjV9TwUldaHGx6H7bMhDMZ0GYAXPESdLnKq18ymX4mnbnRc5m7Zy5pZ9Lo07wPLwx8gYGtBnrlQ+a58fFkrlhJ5ooVnN62DYyhdlgbGt96CwGRkZ4OzyNq8FGnVPmcOpPPy8v38MmGI7RtUp8Fdw+gb9uq1WNEhUrYAuvfhV1fWp+7XwMX3wehfTwbVxmOZx9n1q5ZLNy3kOz8bIaEDuGOnnfQK7iXp0M7izGGMzExZK5YQebKVZzZsweAul270vT++/AfEUndzp28MtlWFk1gSrng59iTPPGZVeqaPLgdj13ehXp1vLd04TaOAti7zEpcR9ZD3QAYcC/0uxsCW3s6ulLFZcQxY9cMFscupsAUeOXDx8bhIGfHDitprVhJ7uHDIEK9Xr0IfuIJ/CNHUKe1d2/nyqQJTKlSZJ3J5+Vle5iz8QjtmjZg4d0DiKiJpa4zWbB9Dmz4L6QegsA2VnP4i26Buv6ejq5Ue1P2Mn3HdL49bD18fE3Ha5jUfRKtA7wjEZi8PLKjoqzqwZUryT9+HHx9adC/P40n3UbDYcOoHazPmxVHE5hSJVgXa93rOpp+mjsvaccjkTWw1JWeYN/f+hhy0iG0n/V+rq6jwMe7Tx/bjm9j2o5p/BD/A/V96zOx20Ru6XYLzep7/hk0R04Op37+mczvVpC1Zg0F6emInx8NLxmMf2QkDYcMwaeRPmtWFu/eA5XygMycPF5eHs3cjUdo37QBi+4ZQJ+wGlbqOrrdvr/1ORgHhI+xXirZup+nIyuVMYafEn5i2o5pbD2+lcC6gdzX6z4mdJ3g8YePCzIzyVr7PZkrV5L144+Y7GxqBQTgf9lQGo4YQcPBg6lVr55HY6xqNIEp5eTHmBM89dkOEtNPc9el7XkksjN+tWtIqcvhgH3fWInr8E9Qx9+6t9X/bggK83R0pcrOy2bpgaXMi55HbFoszes358m+T3Jtp2s9+vBxfnIymatWWc3d12+AvDx8mjWl0ZjR+EdG0qBfP6S2vtzyXGkCUwqr1PXSsj3M2xRH+2YNWHjPQPqEBXk6rMqRewq2z4UN70HKfmjUGi7/p3V/y8+7q7GOZBxhXvQ8FscuJjMvk66Nu/LCwBcY1X6Ux956nJeQQObKlWSsWMHprdvA4aB269Y0vvlm/CMjqdfrQqSK9AHp7TSBqRrv+30n+Otnv3IsI4e7h7Tn4RE1pNSVkQibP4SoGVaPGa0ugnEzIHysV9/fchgH6xLWMTd6Lj8l/ISv+BIZFsmN4TdyYbMLPdKsPDcujoyvvybzuxXk7N4NQN3OnWk6ZQr+kSOo26VLjW7u7i7eu5cq5WYZOXn8c+kePo2Ko2NwQz6bMpDebWpAqSvxV6s14Y5F4MiH8FEw4H5o3R+8+CSbkZvBlzFfMn/vfOIy42haryn3Xngv4zqP80jDjPzUVDK/+Yb0JV9ZDxYD9S68kODHH8N/xAjqhHl3tWt1oAlM1Uhr9x7nr5/vICkjhylDO/Dg8E7Vu9TlcEDsClj/Hzj4A9RuAH0nW/e3Grf3dHSlikmNYV70PJYeWMrp/NP0ataLv/T+CyPajKj0akJHTg5Za9aQvuQrsn78EfLzqdupI80efYRGV19N7VatKjWe3+JyGGrV8t6LD3fRBKZqlPTTefzz690siIqnU3BD3rt3EL1aB3o6LPfJzYZf58P6/1qvMwkIgcgX4KJboZ73ljbzHfmsiVvDvOh5bD62mbo+dbmq3VXc0PUGujXpVqmxmIICsjdvJn3JV2R++y2OU6fwDQ6m8a230mjMaI9UD+bm5JO4P52E6FQS9qVSz78Oo+6/sFJj8AaawFSNsWbvcf762Q6OZ+Zw79AOPFCdS13pCRA1HaI+gtMp0PJCuHaa1d2Thxo3uCL5dDKfx3zOp3s/JSk7iVYNWvFwn4e5tuO1BPpV3oWGMYYze/eSvuQrMr7+mvykJGo1aID/FVfQaMxo6vfti/hU3r6Tn1tA4oF0EvamkrA3leOHMq1Sl4/QvF0ALTt6d2Mbd9EEpqq9XUfTeXPFPlbuOU7n5g15/5ZBXFgdS13GWN07bXwf9nxlPb/V5Srr+a2wgV59f2vnyZ3Mi57H8oPLyXPk0b9lf/7W/28MCR1SqT3C5yUmkr50KRlLvuJMTAz4+tLwkkto9NSTNLzsMmr5+VVKHAV5DpIOpRO/N42EvakcO5iOI98gtYTgMH96RbYhpEsgLTsEUrtuNb0Ic4EmMFVtxR7P4s2V+/j610QC/Hx5/Iou3HFJO+r6VrMDPu+01SBj0/twbIfV9H3AvdD3Dghq6+noSpRbkMu3h75lfvR8fj35K/V963Ndp+uY0HUC7QMr775cQUYGmd99R/qSr8jevBmMoV7v3rR49u/4jxyJb5D7q1oLChycOJxJvF3COrY/nfw8Bwg0a+3PBUNDCekSRKuOgdSpp6ftQrolVLUTl5LN26ti+HxrPH61ffjLsI7ccUl7GtXz3qqzc5IWZ1UTbplpVRMGd4NRb8EF46FOA09HV6Jjp46xcN9CFu1bREpOCm0D2vJUv6cY22EsDes0rJQYHLm5nPrhB6sxxtq1mNxc6rRtS9O/3E+jUaOo06aNe5fvMJyMK0xYaSTGppF3pgCAxq0a0G1wKythdQrEr0E1228rkCYwVW0kZeTwzuoYPt0ch4hw+6B2TBnagSYN63o6tIpjDBxeZ1UTRi+1hnW5CvrfA20He201oTGGLUlbmBs9l9VHVuMwDoaEDmFC+AQubnkxtcT9D/Yah4PT27ZZ97W++QZHejo+jRsTeP31NBozGr8ePdzWGMM4DMlHT5GwN5X4vakkxqZxJjsfgMDm9encvwWhdsKqH+C9b3/2Nm5NYCIyEngb8AGmGWOmFhlfF5gF9AGSgeuNMYdEpC2wB9hrT7rBGHOPO2NVVVdy1hn+9/1+Zq0/TIHDcH3f1vxlWCdaNKqc+xWVIjcbdiyETR9A0k6rBeHAv1jVhIHuLS2cj+y8bJYdXMa86HnsS91HQJ0Abul2C9d3uZ5Q/9BKieHM/v1W0lq6lLyEBKRePfxHjKDRmNE0GDAA8a3406AxhtRj2Vaji32pJOxLIycrD4CApn60792M0C5BhHQOokFgNbrAqmRuS2Ai4gO8C0QC8cBmEVlijNntNNlkINUY01FEbgBeAa63x+03xnjXG+aUV0k/nce0Hw8w46eDnM4r4E+9Q3loRCdaN/Zc33cVLu0IbJ4GW2dZvWU07wGj/w09/wx1vHc9j2Qc4dO9n/JF7Bdk5mbSJagLzw14jqvaX0U9X/d3WJt3/DgZy5aRseQrq2eMWrVoMHAgzR58AP/hw6nVoOKrWNNPZBMfbSWrhL2pZGfkAtAwqC5hPZpYCatLEP6Nq9GFlYe5swTWD4g1xhwAEJH5wFjAOYGNBZ6z/78I+I9ofyuqDNm5+Xy07hAf/HCA9NN5XN2zJQ9HdqJjsHe/l8plxsChn2Dj/6yXR4L1+pL+d0PYIK+sJixwFLDj5A6+j/+etXFriU2LxVd8GRE2ggldJ9A7uLfbn5XKT00la/VqMpYt59T69eBw4NejB83/+hQBV12Fb7OK7a0jP6+Ao/vSOLwzmcM7k0k/cRqA+gF1COkSREjnQEK7BhHQtJ52I+Um7kxgIUCc0+d4oH9J0xhj8kUkHWhij2snItuADOBpY8yPRRcgIncBdwG0cfNNV+V5OXkFzNl4hPfWxnIyK5dhXYN5JLIzPUKqyTMwudnw66dWNeHx3VCvMQx6ECIme+Xbjk/lneLnoz+zNm4tPyX8REpOCj7iw0XNL+KxiMcY2XYkzRs0d2sMeYmJZK5cReaKFWRHRVkd54aE0OTuu2g0ejR121dsa8aMk6ethLUrmYToVPLzHPjUrkVI5yAuGBZKaNfGBLWorwmrknhrI45EoI0xJllE+gBfikh3Y0yG80TGmA+ADwAiIiKMB+JUlSCvwMHCqHjeWR1DYnoOA9o34f1bulSf3uJTD1ud6m6dDTlp0KInjPkP9BwHtb3r/VAJWQmsjVvLD/E/sPnYZvIceQTUCWBwyGCGhA5hUMggt79368yBg2SuWEHmypXk7NgBQJ2OHWhy9134jxiBX7duFZZACvIcHI39vZSVlpQNWPexwge1IqxHE0I6B+Jb01506iXcmcASAOfLxlB7WHHTxIuIL9AISDbGGOAMgDFmi4jsBzoDUW6MV3mZAodhyS8JvLUyhsPJ2fRuE8jrf76QgR2bejq082cMHPweNn4A+5YDAuGjrdaEbS72mmrC4qoGAdoGtOXGrjcypPUQegf3xreW+04lxhhydu0mc+UKMlesJHf/fgD8LriAZo88gv+IEdRt367ClpeRfJoju1I4vDOZ+L2p5J8poJavENI5iB6XhhDWowmNgrVa0Bu4M4FtBjqJSDusRHUDcGORaZYAE4H1wDhgtTHGiEgzIMUYUyAi7YFOwAE3xqq8iDGGb3Ye440V+4g5nkV4ywCmT4xgWNfgqn/SKulgZwAAIABJREFUyD0Fv8yHTR/CiT1QvwkMftiqJmwU4unogLKrBoeEDqFto7ZujcEUFHB661Yy7JJW/tFE8PGhfkQEQRMm4D9iOLVbtKiQZRXkO0jcn87hnckc2ZVMytFTAPg39qNr/xZWKatLUI3u8cJbuS2B2fe07ge+xWpGP8MYs0tEXgCijDFLgOnAbBGJBVKwkhzApcALIpIHOIB7jDEp7opVeQdjDGv3neD17/ayMyGDDs0a8O6NF3FljxZVv6ftlINWa8JtsyEn3eqbcOx/ocd1UNvzrdIKqwa/j/uezUmbyXfk/1Y1OLT1UAa2Guj2qkFHbi7Z69eTuXIlmatWU5CSgtSpQ4NBg/C/734aDruswnrFyEo9w5FdVrVgXHQKeTkF1PIRWnX6//bOPDqu677vn9/sG5bBYCEwgMAFoLiJAEjYEkVSlCzRlh1Vm9NYdhTHjRvbqePYapKebM2pk542zdY6TU5zcpy02Ro3i2TTOV4keVFISrJNiQC4SCTARcIMABIzWAgMZp/bP+7DYBGphcQMOML9nPPOW+bi/d4DHt537u/+7u9Xy9Y7m7lle8iMZVUAor11lU9vb686dsx4GCuVF87F+YOnz3DstUlag16+cN9mHu5uwWGv4Mq1qWk4802d5mnoWbDZYeuD2k3Y9t5VdRPOuwa/P/x9nos8t8Q1eHfb3dzVelfJXYMAhUSC2cOHmXn6GWafe45CIoHN7ydw4ABV7z+If99+7IEbD3nP5wtcOj9tjWVNEI/OAjrE/ZYdIdq3h2jdEsTluVnDAkqHiLyklOpd7eu4HtbeX8twU9E3PMXvf/sMR4ZiNFW7+e2Hd/CR3jZcjgoVrvQMnPkWnHpK19/KZ6C6Fe76Jej9GahenXpRALOZWZ4feZ7nIs9xOHKYyfTkEtfg3W13015d+iKMOtz9e8w8+yyJo0dRmQz2ujqqP/RBqu67D9+ePdhcN56NIjG9qJf1yiSZZA6bTWjuqGHPI5to3xGirsVvelkVjBEww6rwyugV/uDpszz7yiXq/C5+48e28vgd7ZVZ3iSTgLPfhlNPwuAzkEtBVYvOkrH9EQj3gm11BDkyE+G5yHPXdA3uDe+l2lVd8uvIjo0tDXfP53G0NFP72EeoPngQ765dN1yeJJ8tMHZhmuFXdABGbFj3snw1LjbtatCTibfU4TbJcN81mL+koaycGZvhf353kH8eGKXK4+AXD27m3+zbQMBdYY9iNgmDT+ue1tlvQ3YOAk26UOT2R6Ht9lURrWw+y0uXX+JI5AiHo4c5P61jn9ZXr+fxrY9zoPUA3Y3dJXcNAqQvXGDmmWd1uPvAAACuTZsI/ey/peq+g3i231i4ez5f4PLFmWJ+wbHz0+SzBcQmrNtYzR0Pb6R9R4hQOGB6We9SKuytYahExmfSHOof4anjEU5Gr+Bz2fnsPZv41P5N1PgqKNN2NgXnvqNF68w3ITMLvnro+qjuabXfqce5ysxYYozD0cMcjhzmB6M/YC43h9PmZHfTbh7tfLQsrkFVKJC5eJHk8eMk+/qYO/YSmQsXAPDcdhsNTzxB1cH7bmhicSFfYHx4tljUceTcNDkrg3soHGD7/hbCm3UGDHclPVeG68YImKEkJDN5nj49xpMvRzkyFCNfUNwWruE/PrCNh7tbKidDfC4D578HJ5/UaZ3SV3SGjB0fhh2PQvs+sJf33yhbyNJ3uY/DkcMcjh4uBmA0+5t5YOMD7Avv4/bm2/E5S5crsZBIkDxxkmTfceaOHyfV109+ehoAW00N3u4ugh/7mA53b26+LhuqoIhFZnUy3DOTjAxOkUlpwQqu87HlDiuD++ZavAGTwX0tYgTMsGLkC4oXzsV56niUb50cJZHJ01Lj4dN3beSRnjCdTRWSqzCfhfPP6Z7Wq1/X0YSeGtj2oO5pbTgA9vJ+w7+UuMSR6BGORI/wwugLJLIJHDYHuxt389Duh9jfup+NNRtL4ipTSpGNjuje1fHjzPUdJ33mLOS1mLg6NhE4eB++nh68PT241q9HrsN9qpRiYiRhCdYU0cFJ0gldcqSmwUvHe5po3awFy19TIV+ADCXFCJjhhnl17ApPvRzla30jjF1JUeV28MDOFh7uCXP7hrrKmMOVz8HFwzoQ45Wv68zv7mrY8mN6TGvj3eAo37f8bCFL/+V+jkT1WNbZybMANPmauH/9/exv3c8dzXfgd658VvVCJkPq1CmSx/uKgpUfjwFg8/nwdO2k/tOfwtvdjberC3vN9c0PU0oxdWmumL09enaS5IwuOVJV52FDVwOtm2sJ3xokEFz9uXKGmw8jYIbr4tKVFF/ri/Lky1FeHZvBYRMObG7gNx7Yyn1bmyojmrCQ18UhTz0Fpw/BXAxcAV0gcvsj0HEvOMr3TX98brwoWC+OvMhMdgaHOOhp6uGJ3U+wL7yPztrOFe9lZS9f1mLVpwUrdeoUKquFxNnWhn/PHt276u7G3dl53fWzlFJciaWInp0k8uokI2cnSUzrkiP+Wjdt2+oIbw7SeqvO4G4wvBVGwAxvm0Q6x7dOjvHU8ShHz8VQCrraavnig9t5YGdzZYxrFQow/KIe0zr9NUhcBqcPNt+vRavzYNkS6OYKOU7ETnA4cpgj0SO8MvEKAI3eRg6uP8j+8H5ub76dKtfKuV5VLkfqzJli7yrZ10c2qlOUisuFZ8cOgh//qaJgOepvLO/kzESqGHQROTvJ7EQaAG+V0yo5ogXL5BY0XA9GwAxvSi5f4Oi5OE+9HOHbpy6RzOZpq/PyuXs6eKgnzKaGwGpf4luTScDrL8Dgs3D6qzAzCg4PdL5fB2J0vh9cK++KuxqxZIyj0aMciR7h6MhRZjIz2MVOV0MXn9/1efaH97M5uHnFXua5yUmS/f0LgnXiBCqp61Y5Ghvx9vQQ/KnH8fX04N669YYmEKcSWeLRWWIRvYwMTnHFqpHl9jsIbw6y6/1atILNJk2T4cYxAmZ4A0opTo1c4anjUQ71jzA+k6ba4+DhnjCP7grT2x68uV8++SxEjsGFf9EZ34d/CIUs2N26h7X9Ed3jcpdefNP5NKdip3h+5HkORw9zOq7rudZ767n3lnvZF97HnpY9KzKZWGUypM6cJTnQT2pggGT/AJmLF/WHdjuerVup/fCH8fZ04+vpwdHcfF1/x0K+wNSlZFGs4lG9zE6mi208fifrNtWw8+5WwrfWEmoJIJUwFmqoKIyAGYqMTCX5al+Urx6PcvbSLE67cM+tjTy6K8w9WxpxO27Sca1CAS6d1GJ1/jl47XnIJgDRSXP3/DsdOXjLHnCVLrQcdLRg/3g/feN99F/u5/TEaXKFHDax0dXQxed6Psf+8H5urbsVm1z/ROf5yMDUQD/J/gGS/f2kTp9GZfSYkr2hHu/OLmoeeQRvTzfeHTuw+d75vSdnMsSis8QtoYpFZpkcnSOfKwBgswnBZh8tnbWEwgFCrQHqWwP4ql0395ccw7sCI2BrnJlUlm+eHOOpl6O8eCGOUrC7Pch/fngHD+xsptZ3E86vUQomzi8I1sXDMBfXn4U6ofujWrDW7wNfXckuI1vIcnbibFGs+sb7GE2MAuC2u9ke2s7Ht32croYudjftvqFs7vnZWVInThTFKjkwQD6u71ncbjzbtxP82Mfwdu3Eu3MnjpaWdyQg+VyBybE53ZtaJFZzVzLFNt5qF/WtAVq31FEf9hNqDRBs8mN3VmjeSkPFYwRsDZLM5Hn+XIynjkd55vQl0rkC60M+vnDvZh7uaaE9VJ7xoHfEzJh2CZ5/TgvX9LA+XtWix7A2HIANd5W0ptZkapL+8X7dw7rcx8nYSVL5FKDD27sbu/n4to/T3djNrcFbcV7nXDGVy5EeGtJiNdBPsr+fzLnzWrgB14YNBPbtw9O1E+/OLjy3bkacb8+WUoq5KxnikVnds4rOEo8kmBxLUMjr89scQl2zn1u21RFqDeieVVj3qgyGmwkjYGuAbL7AQGSKo0Nxjg7FOP76FJl8gaDPyU/0tvHIrjA9bbU3l8snOaVD3OcFa/xVfdxTCxv2w97P67lZoY6SlCUpqALnps7RN95H3+U+BsYHuHjlIgAOcbA1tJUf3/zjdDV20d3QzTr/9RdXzF66pHtV/f2k+gdInjpVDLSw19bi6dpJ9Qc/iLerG+9tO972vKtcJs/k2NyScap4dLY41wp0+HooHKB9x4JY1Tb5sFdyGRvDmsEI2LuQQkFx5tIMR4diPH8uzg/Ox0lk8ojAtuZqPrF3PXduCnHnpvqbp2xJNgmvv7jgFhztA1UAhxfa9+h8gxsPwLqdJck3OJuZZSA2UHQFnhg/wUx2BoA6Tx1dDV080vkIXQ1dbA9tx+O4vom1hbk5PUm43xq7Ghggd+mS/tDpXAi06OrC27UTZ1vbW36xKOQLTI8niUcTTIzMMjGSID6SYPry3HynDbvTRqjFz/rb6gmF9ThVKBzAEzA5Aw2VixGwdwnDE3McGYpxdCjGC+fixBN67GJDvZ+He8Ls7ahnz8YQQf9N4gbK52DkOFz4vhas4R9CPg02hy4/ctcva7dga++KTyZWSjE8M1zsXfWN9zE0OYRCIQidwU4+uOGDdDd209XQRVvVW4vIVW9xdpb04CDpoSFSJ06SHBggPThYTMHkbGvD19tbFKu3CmNXSjEzkWJiJGGJlBarxUEViE67FGoJ0LG7kboWP/WtAWoafZWREcVgeAcYAatQYrNpnj8X5/mhGEfPxRie0C6nhio3+zvrubOjnr0d9YRrb5KMBokYjA3A2Al47QXtHkxf0Z813Qbv/VktWO17wL2CE3eV4vLcZQanBnl14lU9hnW5n8n0JABVzip2NuzkYPtBuhu6ua3+NgKudxZeX5ibI33uHOnBIdJDQ0XRyo2OFtvYqqrw3nYbVZ/+FJ6dVqBF3bUDTOauZJgYmSVuidX8dtZKZgu6mnBdi5/WLXWEWvzUtfgJNvtxum7SaFGDYYUxAlYhzKSy/PDCBEeH4jx/LsarY9q9VeVxcMfGEJ/cu4G9HfV0NK5y7aNCASYvLIjV/DKz8DInuEFPIJ4PvPDfWLaHeWYyMwxNDTE4OcjZybPF7SuZK8U266vXc1frXXQ3dtPd0M3G2o1vO5y9kEqROX9+QaQswcpGIsU24nLh2rQJX28v7o4O3J2duDs7cIbDV01wm0nmmBhNEI8uuP4mRpaOU7n9DkItAbbcvo66cIC6Fj+hFr8pGWJY8xgBu0lJ5/K8/NoUz5/TbsH+yDT5gsLtsNG7Psgvf+BW9nbUs6OlGsdqDbhnk3D59FKhGjtpzcECxA4NW7RQrbttYbnB0PZMPsOF6QtLRGpwapCxxFixTcAZoKO2gw+s/wCdwU46azvpDHa+rVB2lcmQvnCR9NBgsTeVGRwiMzysBRrA6cS9fj3enbdR++FHcXV04O7owNXWdtVcgblsnsnozJLe1EQ0wcxEqtjG4bZT16zHqbRIBagL+82cKoPhGhgBu0nIFxSnRqaLPawfXZwglS1gE9jZWstnDmxk76Z6drUHVydR7uz40l7VpZMQO6sDLQBcVVqceh5fEKqGLeC8/iziBVUgOhvVAmWJ1NDkEBevXCSvtCvNYXOwsWYju5t201HbwebgZjprO1nnX/eWL32VzZJ5/XXdk7KEKj00pLNXWONU2O242ttxb9lC9QMP4O60hKq9/Q2h69l0nolLSabHk1yJJbkynmQ6Nr+fQhWsMHW7EFznY92mGrbtb7HcfwGqQx6TrcJgeAcYAVsFlFLEExmGJ+Y4GdWi9cL5ONNJ7Tba3BTgsffcwt6Oem7fWEe1p4yuokJBTxJe7gKcXejdUN2qBWrrgwtiVdsO11EDap54Ml4UqMEpLVhDU0Mkc8lim3AgTGewk/fd8j42BzfTUdtBe007Ttub/34KiQSZSJTM66+RGRpaGKu6cAGsrOuI4LrlFlydHbpycEcn7s5OXBvWFwMrlFIkZ7JMjCe58nJcC5MlVtPjySWTfgFcHjvVDV7qWwN09jZRZ41TmTB1g2FlMAJWApRSTCezRCaTDE/M6fXkXHE7MpkkmV0YjA/XevnA9iYdKbgpRGNVmWofZebg8ivLelanFlyANofuRW26Z0GomnbckAtwNjPLhekLRZGa71lNpCaKbYLuIJ3BTh7tfLTo+ttUu+mata9ULkd27BLZyDDZSITMcESvI8NkhyPkJyaWtHe2tuLu6CBw4C49RtXRgWvjRmweD/lcgZl4SvecRpNMn3htQaRiqWIJewAEArVuquu9tO8IUV3vpabBS3WDl5p6L26/w7j+DIYSImp+okiF09vbq44dO1Y2e7Pp3II4LROp6GSSmXRuSfsqj4O2oI+2Oi+tQR9tQb3e3FRFW10JS0lkUzD1GkxehIkLej15AeLnYOLcggvQXb10nGreBfgOQ9gT2QQjsyNEZ6NEZ6OMzI4s2V8cUOF1eNlUs4nOYCcdtR16rCrYSb13aVCHUor81BTZSGRBoIaHyUat7dFRyC36fTscOJubcbW14mxtw9naWtx2b9xA1ubW7r1Fvaf57dmJFIv/JexO24Iw1Xustd6vCnlwVELdM4PhTRCRl5RSvat9HdeDEbBrkMzkiU7NMTyRJDI5x/CktZ7QQjU1l13S3uey0xr0WiLlo9USqNagl7Y6HzXeErkBldJ5AJcL1Pz+zMjS9k4/BNdD3QZo2r7UBfg2RHQuO6dFKTFSFKjFYjWVnlrS3mP30BJooSXQQjgQpiXQQntVO53BTlqrWosRgIV0mmw0agmU7jkVBSoSoTA7u+S89lAIZ2sY1zKBorGFtLuGuZkciak0s1NpElMZvT2pe1bzZern8VY5qa73LhIqva5p8OoACjMuZXgXU8kCtuZdiLPpHIf6Rt4gUrHZ9JJ2LoetKEo7W2uKItVmiVSdv4SRYvmszv33BoG6qNeZmaXtq5q1SG08oEPW5wUruB78DW8qVMlcktHZUSKzkSW9p3nRWuzqA3DZXEVx2hHasUSowoEwdR7tbizMzJCLxciNx8gOjZAdPsRYJEImontTucuXl5xXPJ6iQPne8x4c4TD5hjYygUYy7hrmUkJ8Kk1ifjmZZvZIimxq8A335PI68Ne6CQTddLRXF3tSNQ1eqkNeXN41/29gMFQka/4/N5cv8GtPncBhE1pqvbTVebl3S+OCq69Oi1R9wF3aTAap6av3oCYvwnQE1KKxF7sbgu1anNrvXBCn4AaoveWaJUOyhSyTyXHiyTixZIzRxOiS3lN0NvoGgXLanEVB2hLaQjgQLu63uBqoni1QiE9ocXo9Rm58nFzsRXKxGDPjMSZjMXKxGCq99AsBIjjWrcPV2opr7wFsDbeQDTaT8YZIO6qYyzoWxGkqzVwkY7n2LluLLuXhq3Hhr3VT1+ynbWsd/lq3Fitr7a9143QbN5/B8G5kzbsQlVKMTKdoqnKvzHwqpXQF4NSUFqWktb7W/syYFqzk5NLz+EJv7D3N71c1FyP+svks8VRcL0lrSS2sJ5ITxFNasJa790CHobf4F1x8YX8LrYVaWtJeQkk7/itZCrG4JUwxaxknPx4jPz191V+BBEOoxjCFUAv5YCO5qhA5b5Ccu5qsw0dKuUlm7SSmsySm0mQXB0ZYuH2OogAtF6RArRtfjQtflXHvGQw3SiW7ENe8gF2VfHaZ2Ey+PTGaXwq5Nz+/u1pnVffU6CwUywQqU91CXGWXCNHV1rFkbElQxGJ8Dh8hb4h6Zx0tVNOU81Of91KXcVGbcVCVslE1m8c9PUd+PLYgTvH4Qmg5UBA7WaefnL+OQn2YfG0j+ap6cv4gWXcVWYePjHjIFJyks0I6WSCdvPb92x02vNXOJYK0XKD8tW6TDslgKBOVLGBr3oVIIg7/7yeXilF27s1/xu7SAuS1RMhXD3WbUO5qUp4qkm4fSaePpNOtF7tTLzYbSRHm8imSuSTJXJKZzIwWpZnjxC8/SzwZL2ZBn0eUwpuGxpyPFlVDa85PT95HXbaD2rSDqrTgSyo8czlciQz2RBI1PUP+yjiF2QsoIG93k7d7yDk85O0eZhxeJpx+8rVN5KpvIV/XRbaliqzDT1Y8pJUWpFz2KvdfAGbAkbHj9TvxBPRS63fimd/3O/EGFu1b2w6XzYSWGwyGFWHNC1hWbJyy5ZgLNpJ0tpN0ekg6XCTtDr2IjZRNSKJIUiCp8iTzGZK5JHO5OUuIxkmmXyeVSKFQ2AoKZw5cOXBai2t+Pw/OnLL2hdqCm3VZL9tybmrTDqrT1fiSVXiSeRyJPCQL5NOKnLjIOzzk7J43rKdcfuLeKvLuAHm3j7zfQ67ZQ06c5JSDbMEGvLlouLyOosgE/E4ariI+y4XJVOI1GAyryZoXsOnUHF8+MWoJy7zQqKLwuPN2Qnk3HuXEVXDhyjtxFBw48g7syo4978OWr0IKNigIFGwobCibk4LYKdgcKHFQsDmsbXtxuyAOCnYXebubnMNLwuXnitNLrtZDLuiiIG/DjSbgcttxeR04PQ5cHjterwOX247Tq/ddHodevHaci/bnRcntd5jMEAaDoeIoqYCJyP3AlwA78GWl1O8s+9wN/BWwG4gDH1FKXbQ++1Xgk0Ae+AWl1LdLcY2OGei9/GtFsSkKjtOBcttRYicDZN7yTG8Pm03XY7TbBLtDsDvtuHxOfH4nLo8lQouEpyg43qVCNP+Z02U3gQwGg2FNUjIBExE78CfAQSAC/EhEDimlTi9q9klgUinVISKPAf8N+IiIbAMeA7YDLcCzIrJZKfXGcLUbxFtfT+PuTuxOO3aXA7vbgd3lwOG0Y3MIdodt0SLYrG2bXZYctztsxc+K+3YbdqfVzm7D5hAz/mMwGAwrRCl7YO8FhpRS5wFE5CvAQ8BiAXsI+E/W9j8Cfyz6Df8Q8BWlVBq4ICJD1vleWOmLdPvdfOgL713p0xoMBoOhxJRy4CMMDC/aj1jHrtpGKZUDpoHQ2/xZRORTInJMRI6Nj4+v4KUbDAaD4WanokfulVJ/ppTqVUr1NjQ0rPblGAwGg6GMlFLAokDbov1W69hV24iIA6hBB3O8nZ81GAwGwxqmlAL2I6BTRDaIiAsdlHFoWZtDwE9b2z8OfFfp1CCHgMdExC0iG4BO4IclvFaDwWAwVBglC+JQSuVE5OeBb6PD6P9CKXVKRH4LOKaUOgT8OfDXVpDGBFrksNr9PTrgIwd8thQRiAaDwWCoXEwuRIPBYFjDVHIuxIoO4jAYDAbD2sUImMFgMBgqkneNC1FExoHXbuAU9UBshS6nEuyupu21Znc1bZt7Xhu2b8Ruu1KqIuchvWsE7EYRkWOr4QdeLburaXut2V1N2+ae14bt1bzn1cS4EA0Gg8FQkRgBMxgMBkNFYgRsgT9bY3ZX0/Zas7uats09rw3bq3nPq4YZAzMYDAZDRWJ6YAaDwWCoSIyAGQwGg6EiWfMCJiL3i8gZERkSkV8po902EfmeiJwWkVMi8vly2bbs20XkuIj8c5nt1orIP4rIqyLyiojsKZPdJ6zf80kR+TsR8ZTQ1l+IyGURObnoWJ2IPCMig9Y6WCa7v2f9rgdE5CkRqV1pu9eyveizXxQRJSL15bIrIp+z7vuUiPxuOeyKSLeIvCgifVadwhWvlHut90Y5nq+bkTUtYCJiB/4E+CCwDfioiGwrk/kc8ItKqW3AHcBny2gb4PPAK2W0N8+XgG8ppbYAXeW4BhEJA78A9CqldqCTSz9WQpP/B7h/2bFfAb6jlOoEvmPtl8PuM8AOpdRO4CzwqyWwey3biEgb8H7g9XLZFZF70FXdu5RS24HfL4dd4HeBLyqluoHftPZXmmu9N8rxfN10rGkBA94LDCmlziulMsBX0A9+yVFKjSqlXra2Z9Av8jdUnS4FItIK/Bjw5XLYW2S3BrgLXYUApVRGKTVVJvMOwGvVnfMBI6UypJT6F3R1hcU8BPyltf2XwMPlsKuUetqqdg7wIrq23opzjXsG+O/AfwBKEi12Dbs/B/yOUipttblcJrsKqLa2ayjBM/Ym742SP183I2tdwMLA8KL9CGUSkcWIyHqgB/hBmUz+D/RLpVAme/NsAMaB/225L78sIv5SG1VKRdHfwl8HRoFppdTTpba7jCal1Ki1PQY0ldk+wM8A3yyXMRF5CIgqpfrLZdNiM7BfRH4gIs+JyHvKZPcLwO+JyDD6eStVbxd4w3vjZni+ys5aF7BVR0QCwD8BX1BKXSmDvQeAy0qpl0pt6yo4gF3A/1JK9QAJyuDqsMYDHkILaAvgF5HHS233WlhFW8s6f0VEfh3tfvrbMtnzAb+GdqWVGwdQh3ax/TLw9yIiZbD7c8ATSqk24AksT0MpeLP3xmo8X6vFWhewKNC2aL/VOlYWRMSJfgj/Vin1ZJnM7gUeFJGLaJfp+0Tkb8pkOwJElFLzPc1/RAtaqbkPuKCUGldKZYEngTvLYHcxl0SkGcBar7hb61qIyCeAB4CfVOWb+LkJ/YWh33rWWoGXRWRdGWxHgCeV5odoT8OKB5BchZ9GP1sA/4AeolhxrvHeWLXnazVZ6wL2I6BTRDaIiAs9sH+oHIatb4R/DryilPrDctgEUEr9qlKqVSm1Hn2/31VKlaU3opQaA4ZF5Fbr0L3oqtul5nXgDhHxWb/3eyl/AMsh9AsOa/21chgVkfvR7uIHlVJz5bAJoJQ6oZRqVEqtt561CLDLegZKzVeBewBEZDPgojwZ4keAA9b2+4DBlTbwJu+NVXm+Vh2l1JpegA+ho7POAb9eRrv70N38AaDPWj5U5nu/G/jnMtvsBo5Z9/1VIFgmu18EXgVOAn8NuEto6+/QY21Z9Iv7k0AIHR02CDwL1JXJ7hB6nHf+GfvTct3zss8vAvVlumcX8DfW3/pl4H1lsrsPeAnoR49L7S6B3au+N8rxfN2Mi0klZTAYDIaKZK27EA0Gg8FQoRg19zB/AAAC40lEQVQBMxgMBkNFYgTMYDAYDBWJETCDwWAwVCRGwAwGg8FQkRgBMxgAK1v63yzad4jI+PVk6xeR9VfLyr6sjU1E/sjKjn9CRH4kIhusz75RqqzxBsO7CcdqX4DBcJOQAHaIiFcplQQOUtqsLB9Bp7XaqZQqWAmWEwBKqQ+V0K7B8K7B9MAMhgW+gc7SD/BR9GRVAETkvSLygpWE+Pn5bCIisl1EfmjVgBoQkc7FJxSRjdbPLE8o2wyMKqUKAEqpiFJq0vqZiyJSLyKfsc7bJyIXROR71ufvt67lZRH5BysvnsGw5jACZjAs8BXgMdHFLneytDrAq8B+pZMQ/ybwX6zjnwG+pHQNqF50VgYALJH7J+ATSqkfLbP198C/ssTpD0SkZ/nFKKX+1Drve6zz/qFVFPI3gPuUUrvQWU3+/Y3euMFQiRgXosFgoZQasEpUfBTdG1tMDfCXVg9LAU7r+AvAr1suwCeVUoNW4vMGdD66R5VSb8j3qJSKWAL3Pmv5joj8a6XUd65yaV9C56z8ulVNYBtw1LLjsq7BYFhzGAEzGJZyCF3L6W50frl5fhv4nlLqEUvkvg+glPq/IvIDtOvxGyLyaeA8MI1OIryPayQsVrrg4jeBb4rIJXQRwiUCZmWSbwd+fv4Q8IxS6qM3dpsGQ+VjXIgGw1L+Al0W/sSy4zUsBHV8Yv6giGwEziul/gjd49ppfZQBHgE+LiIfW25ERHaJSIu1bbN+7rVlbXYDvwQ8Pj9Whq6qvFdEOqw2fivjusGw5jACZjAswgqm+KOrfPS7wH8VkeMs9Vz8BHBSRPqAHcBfLTpXAl2H6wkReXDZ+RqBr1vh9gPoYpN/vKzNz6MLM37PGiv7slJqHC2gfyciA2j34Zbru1uDobIx2egNBoPBUJGYHpjBYDAYKhIjYAaDwWCoSIyAGQwGg6EiMQJmMBgMhorECJjBYDAYKhIjYAaDwWCoSIyAGQwGg6Ei+f+nWzgPELSOawAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plt = [0] + x_plt\n",
    "for aps in all_perc_scores:\n",
    "    plt.plot(x_plt, aps)\n",
    "    plt.xticks(x_plt)\n",
    "plt.legend(perc_list)\n",
    "plt.title('Outlier Score for Increasing Mask Size and Different Feature Subsets')\n",
    "plt.xlabel('Mask Size')\n",
    "plt.ylabel('Outlier Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer outlier threshold value\n",
    "\n",
    "Finding good threshold values can be tricky since they are typically not easy to interpret. The `infer_threshold` method helps finding a sensible value. We need to pass a batch of instances `X` and specify what percentage of those we consider to be normal via `threshold_perc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current threshold: 0.015\n",
      "New threshold: 0.010383214280009267\n"
     ]
    }
   ],
   "source": [
    "print('Current threshold: {}'.format(od.threshold))\n",
    "od.infer_threshold(X, threshold_perc=99)  # assume 1% of the training data are outliers\n",
    "print('New threshold: {}'.format(od.threshold))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
